Merge pull request #196 from crocs-muni/issue/195-Docs

Docs

51 files changed, 2872 insertions, 633 deletions

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 00000000..f5df9712
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,48 @@
+name: "Build docs"
+on:
+  push:
+  workflow_dispatch:
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+      - name: Install external dependencies
+        run: sudo apt-get install build-essential libpoppler-cpp-dev pkg-config python3-dev -y
+      - name: Install sec-certs and deps
+        run: |
+          pip install -r requirements/requirements.txt
+          pip install -r requirements/dev_requirements.txt
+          pip install -e .
+      - name: Build docs
+        run: |
+          cd docs
+          make html
+      - name: Save docs artifact
+        uses: actions/upload-artifact@v3
+        with:
+          name: docs
+          path: docs/_build/html/
+          retention-days: 7
+  upload:
+    runs-on: ubuntu-latest
+    needs: build
+    if: github.ref == 'refs/heads/main' && github.repository == 'crocs-muni/sec-certs'  # Potentially change this into a check on release.
+    steps:
+      - name: Get docs artifact
+        uses: actions/download-artifact@v3
+        with:
+          name: docs
+          path: docs
+      - name: Archive docs
+        run: |
+          cd docs
+          zip -r docs.zip *
+      - name: Push docs to website
+        run: |
+          curl -F data=@docs/docs.zip https://seccerts.org/docs/upload?token=${{ secrets.DOCS_AUTH_TOKEN }}
diff --git a/.github/workflows/draft_release.yml b/.github/workflows/draft_release.yml
new file mode 100644
index 00000000..95e2cadc
--- /dev/null
+++ b/.github/workflows/draft_release.yml
@@ -0,0 +1,20 @@
+name: Create release draft
+
+on:
+  push:
+    branches:
+      - "main"
+    tags:
+      - "*.*.*"
+
+jobs:
+  draft-release:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+      - name: Release
+        uses: softprops/action-gh-release@v1
+        with:
+          draft: true
+          generate_release_notes: true
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 9063beaa..3a061417 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -1,5 +1,5 @@
 name: Release (PyPi, DockerHub)
-on: 
+on:
   release:
     types: [published]
 
@@ -27,7 +27,7 @@ jobs:
   docker_release:
     name: Release on DockerHub
     runs-on: ubuntu-latest
-    if: github.repository == 'crocs-muni/sec-certs'  
+    if: github.repository == 'crocs-muni/sec-certs'
     steps:
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v1
@@ -47,6 +47,6 @@ jobs:
         id: docker_build
         uses: docker/build-push-action@v2
         with:
-          file: docker/Dockerfile
+          file: Dockerfile
           push: true
           tags: ${{ steps.meta.outputs.tags }}
diff --git a/.gitignore b/.gitignore
index 3176de71..d9519043 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,8 @@
 # results folder
 results*/
 
+# version file by setuptools-scm
+sec_certs/_version.py
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -128,3 +130,6 @@ cert_processing_log.txt
 
 # Example script may dump classification report into the folder
 examples/classification_report.json
+
+# Experiment results produced by notebooks
+notebooks/cc/results/
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index d539f656..11cd09f0 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,4 +1,4 @@
-## Contributing
+# Contributing
 
 You contribution is warmly welcomed. You can help by:
 
@@ -10,37 +10,47 @@ You contribution is warmly welcomed. You can help by:
 
 ## Dependencies
 
-Our [Dockerfile](https://github.com/crocs-muni/sec-certs/blob/main/docker/Dockerfile) presents all the required dependencies, elaborated below.
-
-- [Java](https://www.java.com/en) is needed to parse tables in FIPS pdf documents, must be available from `PATH`.
-- Some imported libraries have non-trivial dependencies to resolve:
-    - [pdftotext](https://github.com/jalan/pdftotext) requires [Poppler](https://poppler.freedesktop.org/) to be installed. We've experienced issues with older versions of Poppler (`0.x`), make sure to install `20.x` version of these libraries.
-    - [graphviz](https://pypi.org/project/graphviz/) requires `graphviz` to be on the path
+For complete list of system dependencies, see [docs/installation](https://seccerts.org/docs/installation.html).
 
 ### Requirements
 
 Requirements are maintained with [pip-tools](https://github.com/jazzband/pip-tools). The main ideas are:
-- List actual dependencies in `.in` files inside [requirements](requirements) folder without pinning them.
-- Those dependencies are loaded into [setup.py](setup.py) file.
-- Additionally, [compile.sh](requirements/compile.sh) script is used to compile pinned versions of requirements that reside in `.txt` files in the same folder.
+- List actual dependencies in `.in` files inside [requirements](https://github.com/crocs-muni/sec-certs/blob/main/requirements) folder without pinning them.
+- Those dependencies are loaded into [setup.py](https://github.com/crocs-muni/sec-certs/blob/main/setup.py) file.
+- Additionally, [compile.sh](https://github.com/crocs-muni/sec-certs/blob/main/requirements/compile.sh) script is used to compile pinned versions of requirements that reside in `.txt` files in the same folder.
 - Tests, linting and Docker all run against this reproducible environment of pinned requirements.
 
-## Branches and releases
+## Branches
+
+`main` is the default branch against which all pull requests are to be made. This branch is not neccessarily stable, only the releases are.
+
+## Releases and version strings
+
+- On each revision pushed onto `main` that has `*.*.*` tag, a draft release is created with prepared changelog (this step can be skipped and the Release created right from the GitHub GUI).
+- This draft release is to be published manually by the maintainer.
+- Version string is not indexed in `git` but can be retreived maintained by `setuptools-scm` from git tags instead.
+- `setuptools-scm` will automatically, upon editable/real install of a package, infer its version and write it to `sec_certs/_version.py`. This file is not indexed as well. See more at [setuptools-scm GitHub](https://github.com/pypa/setuptools_scm)
+- On publishing a release, the tool is automatically published to [PyPi](https://pypi.org/project/sec-certs/) and [DockerHub](https://hub.docker.com/repository/docker/seccerts/sec-certs).
+
+Note on single-sourcing the package version: More can be read [here](https://packaging.python.org/en/latest/guides/single-sourcing-package-version/). The downside of our approach is that `.git` folder and editable/real install is needed to infer the version of the package. Releases can be infered without installing the project.
+
+### Currently, the release process is as follows
+
+1. (skip this optionally) Tag a revision with `*.*.*` tag -- this will create a draft release in GitHub.
+2. Modify changelog and publish the release (or create it from scratch with new tag).
+3. This will automatically update PyPi and DockerHub packages.
 
-- `main` is the default branch against which all pull requests are to be made. This branch is not neccessarily stable, only the releases are.
-- [Releases](https://github.com/crocs-muni/sec-certs/releases) are prepared manually by tagging revisions of `main` branch with `x.y.z` according to [semantic versioning](https://semver.org).
-- Upon each release, the tool is automatically published to [PyPi](https://pypi.org/project/sec-certs/) and [DockerHub](https://hub.docker.com/repository/docker/seccerts/sec-certs).
 
 ## Quality assurance
 
 All commits shall pass the lint pipeline of the following tools:
 
-- Mypy (see [pyproject.toml](https://github.com/crocs-muni/sec-certs/blob/dev/pyproject.toml) for settings)
-- Black (see [pyproject.toml](https://github.com/crocs-muni/sec-certs/blob/dev/pyproject.toml) for settings)
-- isort (see [pyproject.toml](https://github.com/crocs-muni/sec-certs/blob/dev/pyproject.toml) for settings)
-- Flake8 (see [.flake8](https://github.com/crocs-muni/sec-certs/blob/dev/.flake8) for settings)
+- Mypy (see [pyproject.toml](https://github.com/crocs-muni/sec-certs/blob/main/pyproject.toml) for settings)
+- Black (see [pyproject.toml](https://github.com/crocs-muni/sec-certs/blob/main/pyproject.toml) for settings)
+- isort (see [pyproject.toml](https://github.com/crocs-muni/sec-certs/blob/main/pyproject.toml) for settings)
+- Flake8 (see [.flake8](https://github.com/crocs-muni/sec-certs/blob/main/.flake8) for settings)
 
-These tools can be installed via [dev_requirements.txt](https://github.com/crocs-muni/sec-certs/blob/dev/dev_requirements.txt) You can use [pre-commit](https://pre-commit.com/) tool register git hook that will evalute these checks prior to any commit and abort the commit for you. Note that the pre-commit is not meant to automatically fix the issues, just warn you.
+These tools can be installed via [dev_requirements.txt](https://github.com/crocs-muni/sec-certs/blob/main/dev_requirements.txt) You can use [pre-commit](https://pre-commit.com/) tool register git hook that will evalute these checks prior to any commit and abort the commit for you. Note that the pre-commit is not meant to automatically fix the issues, just warn you.
 
 It should thus suffice to:
 
@@ -54,4 +64,11 @@ To ivoke the tools manually, you can, in the repository root, use:
 - Mypy: `mypy .`
 - Black: `black --check .` (without the flag to reformat)
 - isort: `isort --check-only .` (without the flag to actually fix the issue)
-- Flake8: `flake8 .`
\ No newline at end of file
+- Flake8: `flake8 .`
+
+## Documentation
+
+Every public method of a module that can be leveraged as an API by user should be documented. The docstrng style should
+be `sphinx-oneline`.
+
+The documentation is built using `sphinx` with `mnyst` extension that allows for markdown files. Folder `notebooks/examples` is symbolically linked to `/docs` and its contents will be automatically parsed. These notebooks are supposed to be runnable from Binder.
\ No newline at end of file
diff --git a/docker/Dockerfile b/Dockerfile
index 8731d8ce..ad76f40a 100644
--- a/docker/Dockerfile
+++ b/Dockerfile
@@ -1,6 +1,7 @@
-FROM ubuntu:jammy
+FROM ubuntu:jammy-20220428
 
 ENV USER="user"
+ENV NB_UID=1000
 ENV HOME /home/${USER}
 
 #installing dependencies
@@ -20,7 +21,10 @@ RUN apt-get install graphviz -y
 
 RUN adduser --disabled-password \
   --gecos "Default user" \
+  --uid ${NB_UID} \
   ${USER}
+
+RUN chown -R ${NB_UID} ${HOME}
 USER ${USER}
 WORKDIR ${HOME}
 
@@ -43,7 +47,8 @@ RUN \
   pip3 install -U pip && \
   pip3 install wheel && \
   pip3 install -r requirements/requirements.txt && \
-  pip3 install --no-cache notebook
+  pip3 install --no-cache notebook jupyterlab && \
+  pip3 install -e .
 
 # Download spacy language model
 RUN python3 -m spacy download en_core_web_sm
diff --git a/LICENSE b/LICENSE
index 5359145a..55e4ec93 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2019 Petr Svenda
+Copyright (c) 2019-2022 Petr Svenda, Adam Janovsky, Jan Jancar, Jiri Michalik, Stanislav Bobon
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index 29da0021..c4b19802 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,6 @@
-# ![](docs/_static/logo.svg)
+# Sec-certs
+
+![](docs/_static/logo.svg)
 
 Tool for analysis of security certificates and their security targets (Common Criteria, NIST FIPS140-2...).
 
@@ -13,27 +15,7 @@ This project is developed by the [Centre for Research On Cryptography and Securi
 
 ## Installation
 
-The tool can be pulled as a docker image with
-
-```bash
-docker pull seccerts/sec-certs
-```
-
-Alternatively, it can be installed from PyPi with
-
-```bash
-pip install -U sec-certs
-```
-
-Note, however, that `Python>=3.8` is required and there are some [additional dependencies](https://github.com/crocs-muni/sec-certs/blob/main/CONTRIBUTING.md#dependencies).
-
-The stable release is also published on [GitHub](https://github.com/crocs-muni/sec-certs/releases) from where it can be setup for development with
-
-```bash
-python3 -m venv venv
-source venv/bin/activate
-pip install -e .
-```
+Use Docker with `docker pull seccerts/sec-certs` or just `pip install -U sec-certs`. For more elaborate description, see [docs](https://seccerts.org/docs/installation.html)
 
 ## Usage (CC)
 
diff --git a/cc_cli.py b/cc_cli.py
index b24e2977..1c28fdab 100755
--- a/cc_cli.py
+++ b/cc_cli.py
@@ -24,8 +24,10 @@ logger = logging.getLogger(__name__)
 @click.option(
     "-o",
     "--output",
-    type=click.Path(file_okay=False, dir_okay=True, writable=True, readable=True),
+    type=click.Path(file_okay=False, dir_okay=True, writable=True, readable=True, resolve_path=True),
     help="Path where the output of the experiment will be stored. May overwrite existing content.",
+    default=Path("./cc_dset/"),
+    show_default=True,
 )
 @click.option(
     "-c",
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 00000000..d4bb2cbb
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/api/dataset.md b/docs/api/dataset.md
new file mode 100644
index 00000000..168fa12a
--- /dev/null
+++ b/docs/api/dataset.md
@@ -0,0 +1,28 @@
+# Dataset package
+
+```{eval-rst}
+.. automodule:: sec_certs.dataset
+    :no-members:
+```
+
+This documentation doesn't provide full API reference for all members of `dataset` package. Instead, it concentrattes on the Dataset that are immediately exposed to the users. Namely, we focus on `CCDataset` and `FIPSDataset`.
+
+```{tip}
+The examples related to this package can be found at [common criteria notebook](./../notebooks/examples/common_criteria.ipynb) and [fips notebook](./../notebooks/examples/fips.ipynb).
+```
+
+## CCDataset
+
+```{eval-rst}
+.. currentmodule:: sec_certs.dataset
+.. autoclass:: CCDataset
+    :members:
+```
+
+## FIPSDataset
+
+```{eval-rst}
+.. currentmodule:: sec_certs.dataset
+.. autoclass:: FIPSDataset
+    :members:
+```
diff --git a/docs/api/model.md b/docs/api/model.md
new file mode 100644
index 00000000..406d24be
--- /dev/null
+++ b/docs/api/model.md
@@ -0,0 +1,42 @@
+# Model package
+
+```{eval-rst}
+.. automodule:: sec_certs.model
+    :no-members:
+```
+
+```{tip}
+The examples related to this package can be found at [model notebook](./../notebooks/examples/model.ipynb).
+```
+
+## CPEClassifier
+
+```{eval-rst}
+.. currentmodule:: sec_certs.model
+.. autoclass:: CPEClassifier
+    :members:
+```
+
+## SARTranformer
+
+```{eval-rst}
+.. currentmodule:: sec_certs.model
+.. autoclass:: SARTransformer
+    :members:
+```
+
+## DependencyFinder
+
+```{eval-rst}
+.. currentmodule:: sec_certs.model
+.. autoclass:: DependencyFinder
+    :members:
+```
+
+## DependencyVulnerabilityFinder
+
+```{eval-rst}
+.. currentmodule:: sec_certs.model
+.. autoclass:: DependencyVulnerabilityFinder
+    :members:
+```
\ No newline at end of file
diff --git a/docs/api/sample.md b/docs/api/sample.md
new file mode 100644
index 00000000..4ff17be1
--- /dev/null
+++ b/docs/api/sample.md
@@ -0,0 +1,26 @@
+# Sample package
+
+```{eval-rst}
+.. automodule:: sec_certs.sample
+    :no-members:
+```
+
+```{tip}
+The examples related to this package can be found at [common criteria notebook](./../notebooks/examples/common_criteria.ipynb) and [fips notebook](./../notebooks/examples/fips.ipynb).
+```
+
+## CommonCriteriaCert
+
+```{eval-rst}
+.. currentmodule:: sec_certs.sample
+.. autoclass:: CommonCriteriaCert
+    :members:
+```
+
+## FIPSCertificate
+
+```{eval-rst}
+.. currentmodule:: sec_certs.sample
+.. autoclass:: FIPSCertificate
+    :members:
+```
\ No newline at end of file
diff --git a/docs/code_of_conduct.md b/docs/code_of_conduct.md
new file mode 100644
index 00000000..74e5474b
--- /dev/null
+++ b/docs/code_of_conduct.md
@@ -0,0 +1,4 @@
+```{include} ../CODE_OF_CONDUCT.md
+:relative-docs: docs/
+:relative-images:
+```
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 00000000..d1a81b2f
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,73 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath("."))
+from importlib.metadata import version as get_version
+
+# -- Project information -----------------------------------------------------
+
+project = "sec-certs"
+copyright = "2020-2022, Adam Janovsky, Petr Svenda, Jan Jancar, Jiri Michalik, Stanislav Bobon."
+# author = "Adam Janovsky, Petr Svenda, Jan Jancar, Jiri Michalik, Stanislav Bobon"
+
+# Note thas this inference won't work from Docker: https://github.com/pypa/setuptools_scm/#usage-from-docker
+release = ".".join(get_version("sec-certs").split(".")[:3])
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = ["myst_nb", "sphinx.ext.autodoc", "sphinx_design", "sphinx_copybutton"]
+
+# Don't exeute notbooks
+nb_execution_mode = "off"
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# Don't show typehints in autodoc files
+autodoc_typehints = "none"
+
+# This is recommended by sphinx_design extension
+myst_enable_extensions = ["colon_fence"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_book_theme"
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+html_logo = "_static/logo.svg"
+html_favicon = "_static/logo_badge.svg"
+
+html_theme_options = {
+    "repository_url": "https://github.com/crocs-muni/sec-certs",
+    "repository_branch": "main",
+    "launch_buttons": {"binderhub_url": "https://mybinder.org"},
+    "use_fullscreen_button": False,
+}
+
+myst_heading_anchors = 3
diff --git a/docs/configuration.md b/docs/configuration.md
new file mode 100644
index 00000000..93c926ba
--- /dev/null
+++ b/docs/configuration.md
@@ -0,0 +1,26 @@
+---
+file_format: mystnb
+mystnb:
+  remove_code_source: true
+  execution_mode: 'force'
+---
+# Configuration
+
+The configuration is stored in yaml file `settings.yaml` at `sec_certs.config` package. Below are the supported options, descriptions and default values.
+
+
+```{code-cell} python
+from sec_certs.config import configuration
+from myst_nb import glue
+from IPython.display import Markdown
+
+cfg = configuration.config
+text = ""
+for key in cfg.__dict__:
+    text += f"`{key}`\n\n- Description: {cfg.get_desription(key)}\n"
+    text += f"- Default value: `{cfg.__getattribute__(key)}`\n\n"
+glue("text", Markdown(text))
+```
+```{glue:md} text
+:format: myst
+```
diff --git a/docs/contributing.md b/docs/contributing.md
new file mode 100644
index 00000000..fc1b2138
--- /dev/null
+++ b/docs/contributing.md
@@ -0,0 +1,4 @@
+```{include} ../CONTRIBUTING.md
+:relative-docs: docs/
+:relative-images:
+```
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 00000000..4760ed1c
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,64 @@
+# Sec-certs documentation
+
+Welcome to the technical documentation of *sec-certs* tool for the data analysis of products certified with Common Criteria or FIPS 140 frameworks. If you're looking for general description of the tool, its use cases and capabilites, we refer you to [sec-certs homepage](https://seccerts.org/). If you are looking for more advanced knowledge, e.g. how to mine your own data, how to extend the tool, and so forth, this is the right place.
+
+There are three main parts of this documentation. *User's guide* describes high-level use of our tool. Driven by this knowledge, you can progress to *Notebook examples* that showcase some of the API that we use in the form of Jupyter notebooks. The documentation also contains some of the modules documented with `autodoc`, see *API reference*. Still, some dark corners of our codebase are not documented. To inspect the code directly, see the [sec_certs](https://github.com/crocs-muni/sec-certs/tree/main/sec_certs) module. If you want, you can run the notebooks as they are stored in the [project repository](https://github.com/crocs-muni/sec-certs/tree/main/notebooks). If you are interested in contributing to our project or in other aspects of our development, you can consult the relevant *GitHub artifacts*
+
+```{button-ref} quickstart
+:align: center
+:color: primary
+:ref-type: myst
+Show me Quickstart!
+```
+
+```{admonition} Launch notebooks in MyBinder
+Each of the notebooks can be launched interactively in MyBinder by clicking on 🚀 icon (top-right corner).
+```
+
+```{toctree}
+:hidden:
+:caption: Navigation
+:maxdepth: 1
+Seccerts homepage <https://seccerts.org/>
+Seccerts docs <https://seccerts.org/docs>
+GitHub repo <https://github.com/crocs-muni/sec-certs>
+Seccerts PyPi <https://pypi.org/project/sec-certs/>
+```
+
+```{toctree}
+:hidden: True
+:caption: User's guide
+:maxdepth: 1
+installation.md
+quickstart.md
+tutorial.md
+configuration.md
+```
+
+```{toctree}
+:caption: Notebook examples
+:hidden: True
+:maxdepth: 1
+notebooks/examples/common_criteria.ipynb
+notebooks/examples/fips.ipynb
+notebooks/examples/model.ipynb
+```
+
+```{toctree}
+:caption: API reference
+:hidden: True
+:maxdepth: 1
+api/sample.md
+api/dataset.md
+api/model.md
+```
+
+```{toctree}
+:maxdepth: 1
+:hidden: True
+:caption: GitHub artifacts
+readme.md
+contributing.md
+code_of_conduct.md
+license.md
+```
\ No newline at end of file
diff --git a/docs/installation.md b/docs/installation.md
new file mode 100644
index 00000000..71e685db
--- /dev/null
+++ b/docs/installation.md
@@ -0,0 +1,47 @@
+# Installation
+
+::::{tab-set}
+:::{tab-item} PyPi (pip)
+
+The tool can be installed from PyPi with
+
+```bash
+pip install -U sec-certs
+```
+
+Note, that `Python>=3.8` is required.
+
+:::
+
+:::{tab-item} Docker
+
+The tool can be pulled as a docker image with
+
+```bash
+docker pull seccerts/sec-certs
+```
+
+:::
+:::{tab-item} Build from sources
+
+The stable release is also published on [GitHub](https://github.com/crocs-muni/sec-certs/releases) from where it can be setup for development with
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+pip install -e .
+```
+
+Alternatively, our Our [Dockerfile](https://github.com/crocs-muni/sec-certs/blob/main/Dockerfile) represents a reproducible way of setting up the environment.
+
+:::
+::::
+
+If you're not using Docker, you must install the dependencies as described below.
+
+## Dependencies
+
+- [Java](https://www.java.com/en) is needed to parse tables in FIPS pdf documents, must be available from `PATH`.
+- Some imported libraries have non-trivial dependencies to resolve:
+    - [pdftotext](https://github.com/jalan/pdftotext) requires [Poppler](https://poppler.freedesktop.org/) to be installed. We've experienced issues with older versions of Poppler (`0.x`), make sure to install `20.x` version of these libraries.
+    - [graphviz](https://pypi.org/project/graphviz/) requires `graphviz` to be on the path
\ No newline at end of file
diff --git a/docs/license.md b/docs/license.md
new file mode 100644
index 00000000..4f6e8060
--- /dev/null
+++ b/docs/license.md
@@ -0,0 +1,6 @@
+# License
+
+```{include} ../LICENSE
+:relative-docs: docs/
+:relative-images:
+```
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 00000000..32bb2452
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF

+

+pushd %~dp0

+

+REM Command file for Sphinx documentation

+

+if "%SPHINXBUILD%" == "" (

+	set SPHINXBUILD=sphinx-build

+)

+set SOURCEDIR=.

+set BUILDDIR=_build

+

+%SPHINXBUILD% >NUL 2>NUL

+if errorlevel 9009 (

+	echo.

+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx

+	echo.installed, then set the SPHINXBUILD environment variable to point

+	echo.to the full path of the 'sphinx-build' executable. Alternatively you

+	echo.may add the Sphinx directory to PATH.

+	echo.

+	echo.If you don't have Sphinx installed, grab it from

+	echo.https://www.sphinx-doc.org/

+	exit /b 1

+)

+

+if "%1" == "" goto help

+

+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%

+goto end

+

+:help

+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%

+

+:end

+popd

diff --git a/docs/notebooks b/docs/notebooks
new file mode 120000
index 00000000..7e2fa2a0
--- /dev/null
+++ b/docs/notebooks
@@ -0,0 +1 @@
+./../notebooks
\ No newline at end of file
diff --git a/docs/quickstart.md b/docs/quickstart.md
new file mode 100644
index 00000000..e16a570d
--- /dev/null
+++ b/docs/quickstart.md
@@ -0,0 +1,56 @@
+# Quickstart
+
+::::{tab-set}
+
+:::{tab-item} Common Criteria
+1. Install the latest version with `pip install -U sec-certs` (see [installation](installation.md)).
+2. Use
+```python
+from sec_certs.dataset import CCDataset
+
+dset = CCDataset.from_web_latest()
+```
+to obtain to obtain freshly processed dataset from [seccerts.org](https://seccerts.org).
+
+3. Play with the dataset. See [example notebook](./notebooks/examples/common_criteria.ipynb).
+:::
+
+:::{tab-item} FIPS 140
+1. Install the latest version with `pip install -U sec-certs` (see [installation](installation.md)).
+2. Use
+```python
+from sec_certs.dataset import FIPSDataset
+
+dset = FIPSDataset.from_web_latest()
+```
+to obtain to obtain freshly processed dataset from [seccerts.org](https://seccerts.org).
+
+3. Play with the dataset. See [example notebook](./notebooks/examples/fips.ipynb).
+:::
+::::
+
+```{hint}
+You can work with those with the help of the [common criteria notebook](notebooks/examples/common_criteria.ipynb) or [fips notebook](notebooks/examples/fips.ipynb) and even launch them in MyBinder without installing anything. Just use the 🚀 icon (top-right corner).
+```
+
+If you insist on processing the whole certificates pipeline, make sure that you installed all [dependencies](installation.md#dependencies). Then, run
+
+::::{tab-set}
+:::{tab-item} Common Criteria
+```bash
+$ cc-certs all
+```
+:::
+
+:::{tab-item} FIPS 140
+```bash
+$ fips-certs new-run
+```
+:::
+::::
+
+This script takes a long time to run (few hours) and will create `./cc_dset` or `./fips_dset` directory. To see all options, call the entrypoint with `--help`.
+
+:::{hint}
+If you installed the docker image, use `docker run -it sec-certs bash` to run the container interactively.
+:::
diff --git a/docs/readme.md b/docs/readme.md
new file mode 100644
index 00000000..2cb706b3
--- /dev/null
+++ b/docs/readme.md
@@ -0,0 +1,4 @@
+```{include} ../README.md
+:relative-docs: docs/
+:relative-images:
+```
diff --git a/examples/cc_oop_demo.py b/examples/cc_oop_demo.py
deleted file mode 100644
index 4a67c6ff..00000000
--- a/examples/cc_oop_demo.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import logging
-from datetime import datetime
-from pathlib import Path
-
-from sec_certs.config.configuration import config
-from sec_certs.dataset import CCDataset
-
-logger = logging.getLogger(__name__)
-
-
-def main():
-    file_handler = logging.FileHandler(config.log_filepath)
-    stream_handler = logging.StreamHandler()
-    formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-    file_handler.setFormatter(formatter)
-    stream_handler.setFormatter(formatter)
-    logging.basicConfig(level=logging.INFO, handlers=[file_handler, stream_handler])
-    start = datetime.now()
-
-    # Create empty dataset
-    dset = CCDataset({}, Path("./debug_dataset"), "cc_full_dataset", "sample dataset description")
-
-    # Load metadata for certificates from CSV and HTML sources
-    dset.get_certs_from_web(to_download=True)
-
-    # explicitly dump to json
-    dset.to_json(dset.json_path)
-
-    # Retrieve protection profile IDs
-    dset.process_protection_profiles()
-
-    # Load dataset from JSON
-    new_dset = CCDataset.from_json("./debug_dataset/cc_full_dataset.json")
-    assert dset == new_dset
-
-    # Download pdfs and update json
-    dset.download_all_pdfs()
-
-    # Convert pdfs to text and update json
-    dset.convert_all_pdfs()
-
-    # Extract data from txt files and update json
-    dset._extract_data()
-
-    # transform to pandas DataFrame
-    # df = dset.to_pandas()
-
-    # Compute heuristics on the dataset
-    dset._compute_heuristics()
-
-    # Compute related cves
-    # dset.compute_related_cves()
-
-    end = datetime.now()
-    logger.info(f"The computation took {(end-start)} seconds.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/fips_oop_demo.py b/examples/fips_oop_demo.py
index 26e81d83..73d5c5c5 100644
--- a/examples/fips_oop_demo.py
+++ b/examples/fips_oop_demo.py
@@ -1,70 +1,38 @@
 import logging
 from datetime import datetime
-from pathlib import Path
-
-import click
 
 from sec_certs.config.configuration import config
-from sec_certs.dataset import FIPSAlgorithmDataset, FIPSDataset
+from sec_certs.dataset import FIPSDataset
 
 logger = logging.getLogger(__name__)
 
 
-@click.command()
-@click.option("--config-file", help="Path to config file")
-@click.option("--json-file", help="Path to dataset json file")
-@click.option("--no-download-algs", help="Redo scan of html files", is_flag=True)
-@click.option("--redo-web-scan", help="Redo scan of PDF files", is_flag=True)
-@click.option("--redo-keyword-scan", help="Don't download algs", is_flag=True)
-@click.option(
-    "--higher-precision-results",
-    help="Redo table search for certificates with high error rate. Behaviour undefined if used on a newly instantiated dataset.",
-    is_flag=True,
-)
-def main(config_file, json_file, no_download_algs, redo_web_scan, redo_keyword_scan, higher_precision_results):
-    logging.basicConfig(level=logging.INFO)
+def main():
+    file_handler = logging.FileHandler(config.log_filepath)
+    stream_handler = logging.StreamHandler()
+    formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+    file_handler.setFormatter(formatter)
+    stream_handler.setFormatter(formatter)
+    logging.basicConfig(level=logging.INFO, handlers=[file_handler, stream_handler])
     start = datetime.now()
 
-    # Load config
-    config.load(config_file if config_file else "../sec_certs/settings.yaml")
-
-    # Create empty dataset
-    dset = FIPSDataset({}, Path("../fips_dataset"), "sample_dataset", "sample dataset description")
-
-    # this is for creating test dataset, usually with small number of pdfs
-    # dset = FIPSDataset({}, Path('./fips_test_dataset'), 'small dataset', 'small dataset for keyword testing')
-
-    # Load metadata for certificates from CSV and HTML sources
-    dset.get_certs_from_web(redo=redo_web_scan)
-
-    logger.info(f"Finished parsing. Have dataset with {len(dset)} certificates.")
-    logger.info(f"Dataset saved to {dset.root_dir}/fips_full_dataset.json")
-
-    logger.info("Converting pdfs")
+    # Full processing of FIPS dataset, fresh run
+    dset = FIPSDataset()
+    dset.get_certs_from_web()
     dset.convert_all_pdfs()
+    dset.pdf_scan()
+    dset.extract_certs_from_tables()
+    dset.finalize_results()
 
-    logger.info("Extracting keywords now.")
-    dset.pdf_scan(redo=redo_keyword_scan)
-
-    logger.info(f"Finished extracting certificates for {len(dset.certs)} items.")
-
-    logger.info("Searching for tables in pdfs")
-
-    not_decoded_files = dset.extract_certs_from_tables(higher_precision_results)
-
-    logger.info(f"Done. Files not decoded: {not_decoded_files}")
-    logger.info("Parsing algorithms")
-    if not no_download_algs:
-        aset = FIPSAlgorithmDataset({}, Path(dset.root_dir / "web/algorithms"), "algorithms", "sample algs")
-        aset.get_certs_from_web()
-        logger.info(f"Finished parsing. Have algorithm dataset with {len(aset)} algorithm numbers.")
-
-        dset.algorithms = aset
+    # TODO: Resolve https://github.com/crocs-muni/sec-certs/issues/210 and refactor this
+    # if not no_download_algs:
+    #     aset.get_certs_from_web()
+    #     logger.info(f"Finished parsing. Have algorithm dataset with {len(aset)} algorithm numbers.")
+    #     dset.algorithms = aset
 
-    logger.info("finalizing results.")
-    dset.finalize_results()
+    # TODO: Resolve https://github.com/crocs-muni/sec-certs/issues/211 and uncomment
+    # dset.plot_graphs(show=False)
 
-    dset.plot_graphs(show=False)
     end = datetime.now()
     logger.info(f"The computation took {(end - start)} seconds.")
 
diff --git a/fips_cli.py b/fips_cli.py
index 8c4cea43..5c95b5ae 100755
--- a/fips_cli.py
+++ b/fips_cli.py
@@ -27,8 +27,10 @@ logger = logging.getLogger(__name__)
 @click.option(
     "-o",
     "--output",
-    type=click.Path(file_okay=False, dir_okay=True, writable=True, readable=True),
+    type=click.Path(file_okay=False, dir_okay=True, writable=True, readable=True, resolve_path=True),
     help="Path to the directory where the output of the 'build' or 'new-run' actions will be stored.",
+    default=Path("./fips_dset/"),
+    show_default=True,
 )
 @click.option(
     "-c",
diff --git a/notebooks/cc/vulnerabilities.ipynb b/notebooks/cc/vulnerabilities.ipynb
index dc76d2c5..05392897 100644
--- a/notebooks/cc/vulnerabilities.ipynb
+++ b/notebooks/cc/vulnerabilities.ipynb
@@ -26,20 +26,25 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
     "from typing import Optional\n",
     "from tqdm.notebook import tqdm\n",
     "\n",
     "from typing import Set\n",
     "from sec_certs.sample.sar import SAR\n",
-    "plt.style.use(\"ggplot\")\n",
+    "plt.style.use(\"seaborn-whitegrid\")\n",
     "\n",
     "from sec_certs.dataset import CCDataset, CVEDataset, CCDatasetMaintenanceUpdates\n",
     "from sec_certs.sample import CommonCriteriaCert\n",
     "import datetime\n",
     "\n",
+    "from pathlib import Path\n",
     "import itertools\n",
     "\n",
-    "from sec_certs.pandas_helpers import compute_cve_correlations, find_earliest_maintenance_after_cve, expand_cc_df_with_cve_cols, compute_maintenances_that_should_fix_vulns, move_fixing_mu_to_directory"
+    "from sec_certs.pandas_helpers import compute_cve_correlations, find_earliest_maintenance_after_cve, expand_cc_df_with_cve_cols, compute_maintenances_that_should_fix_vulns, move_fixing_mu_to_directory\n",
+    "\n",
+    "RESULTS_DIR = Path(\"./results\")\n",
+    "RESULTS_DIR.mkdir(exist_ok=True)"
    ]
   },
   {
@@ -58,18 +63,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "parsing cpe matching (by NIST) dictionary: 100%|██████████| 377628/377628 [00:34<00:00, 10810.92it/s]\n",
-      "Building-up lookup dictionaries for fast CVE matching: 100%|██████████| 183838/183838 [00:14<00:00, 12615.84it/s]\n"
+      "parsing cpe matching (by NIST) dictionary: 100%|██████████| 383657/383657 [00:36<00:00, 10495.53it/s]\n",
+      "Building-up lookup dictionaries for fast CVE matching: 100%|██████████| 187352/187352 [00:14<00:00, 13011.72it/s]\n"
      ]
     }
    ],
    "source": [
     "# Download and save fresh snapshot\n",
     "# dset: CCDataset = CCDataset.from_web_latest()\n",
-    "# dset.to_json(\"/Users/adam/phd/projects/certificates/datasets/from_web_latest/latest.json\")\n",
+    "# dset.to_json(RESULTS_DIR / \"cc_dset.json\")\n",
     "\n",
     "# Local instantiation\n",
-    "dset: CCDataset = CCDataset.from_json('/Users/adam/phd/projects/certificates/sec-certs/datasets/from_web_latest/dataset.json')\n",
+    "dset: CCDataset = CCDataset.from_json(RESULTS_DIR / \"cc_dset.json\")\n",
+    "# dset.process_maintenance_updates() # Run this only once, can take ~10 minutes to finnish, fully processes maintenance updates\n",
     "main_dset = dset.mu_dataset\n",
     "cve_dset: CVEDataset = dset._prepare_cve_dataset()\n",
     "\n",
@@ -102,6 +108,7 @@
    "outputs": [],
    "source": [
     "df = dset.to_pandas()\n",
+    "df = df.loc[df.year_from < 2022] # Cut current year that biases the analysis\n",
     "\n",
     "if 'n_maintenances' not in df.columns:\n",
     "    n_maintenances = main_dset.get_n_maintenances_df()\n",
@@ -111,11 +118,10 @@
     "    main_dates = main_dset.get_maintenance_dates_df()\n",
     "    df = pd.concat([df, main_dates], axis='columns')\n",
     "\n",
-    "# Limit CVE DataFrame to relevant CVEs\n",
+    "# Expand DataFrame with CVEs that affect some certificate\n",
     "cves = list(itertools.chain.from_iterable([x.heuristics.related_cves for x in dset if x.heuristics.related_cves]))\n",
     "cve_dict = {x: cve_dset[x] for x in cves}\n",
-    "cve_dset.cves = cve_dict\n",
-    "\n",
+    "cve_dset.cves = cve_dict # Limit cve_dset to CVEs relevant to some certificate\n",
     "df = expand_cc_df_with_cve_cols(df, cve_dset)"
    ]
   },
@@ -148,44 +154,50 @@
        "      <th>related_cves</th>\n",
        "      <th>cve_published_dates</th>\n",
        "      <th>earliest_cve</th>\n",
-       "      <th>worst_cve</th>\n",
+       "      <th>worst_cve_score</th>\n",
+       "      <th>avg_cve_score</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>c4bd865f2ae51533</th>\n",
-       "      <td>{CVE-2019-5408}</td>\n",
-       "      <td>[2019-08-09]</td>\n",
-       "      <td>2019-08-09</td>\n",
-       "      <td>6.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>ebd276cca70fd723</th>\n",
-       "      <td>{CVE-2019-4513, CVE-2017-1732}</td>\n",
-       "      <td>[2019-08-26, 2018-08-17]</td>\n",
-       "      <td>2018-08-17</td>\n",
-       "      <td>8.2</td>\n",
+       "      <td>{CVE-2013-5421, CVE-2017-1732, CVE-2013-5420, ...</td>\n",
+       "      <td>[2013-12-22, 2018-08-17, 2013-12-23, 2013-12-2...</td>\n",
+       "      <td>2013-12-22</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>5.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7a53ce3f91bf73c7</th>\n",
-       "      <td>{CVE-2017-14800, CVE-2017-9276, CVE-2017-14799...</td>\n",
-       "      <td>[2018-03-01, 2018-03-02, 2018-03-01, 2018-03-0...</td>\n",
+       "      <td>{CVE-2017-14800, CVE-2017-14802, CVE-2017-1480...</td>\n",
+       "      <td>[2018-03-01, 2018-03-02, 2018-03-02, 2018-03-0...</td>\n",
        "      <td>2018-03-01</td>\n",
        "      <td>6.1</td>\n",
+       "      <td>6.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>b2aea954a265af60</th>\n",
-       "      <td>{CVE-2021-44168, CVE-2021-42757, CVE-2021-2610...</td>\n",
-       "      <td>[2022-01-04, 2021-12-08, 2021-12-08, 2021-08-0...</td>\n",
+       "      <th>28819ed8f96586bc</th>\n",
+       "      <td>{CVE-2021-41032, CVE-2021-43206, CVE-2021-2610...</td>\n",
+       "      <td>[2022-05-04, 2022-05-04, 2021-12-08, 2021-12-0...</td>\n",
        "      <td>2020-09-24</td>\n",
        "      <td>9.8</td>\n",
+       "      <td>6.866667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28819ed8f96586bc</th>\n",
-       "      <td>{CVE-2021-44168, CVE-2021-42757, CVE-2021-2610...</td>\n",
-       "      <td>[2022-01-04, 2021-12-08, 2021-12-08, 2021-08-0...</td>\n",
-       "      <td>2020-09-24</td>\n",
+       "      <th>ac5e56e41a0b950e</th>\n",
+       "      <td>{CVE-2019-17655, CVE-2021-43081, CVE-2019-1765...</td>\n",
+       "      <td>[2020-06-16, 2022-05-11, 2021-04-12, 2022-03-0...</td>\n",
+       "      <td>2019-08-23</td>\n",
        "      <td>9.8</td>\n",
+       "      <td>7.019048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1f061d2b3f2e51d1</th>\n",
+       "      <td>{CVE-2018-13374, CVE-2018-13376, CVE-2017-1418...</td>\n",
+       "      <td>[2019-01-22, 2018-11-27, 2018-05-25, 2017-08-1...</td>\n",
+       "      <td>2017-08-10</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6.778000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -193,20 +205,23 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5f1df5ad8e51ba75</th>\n",
-       "      <td>{CVE-2007-0411, CVE-2005-4704, CVE-2006-0427, ...</td>\n",
-       "      <td>[2007-01-23, 2005-12-31, 2006-01-25, 2007-01-2...</td>\n",
+       "      <td>{CVE-2003-1221, CVE-2006-2469, CVE-2006-0430, ...</td>\n",
+       "      <td>[2003-12-31, 2006-05-19, 2006-01-25, 2004-04-1...</td>\n",
        "      <td>2003-08-27</td>\n",
        "      <td>10.0</td>\n",
+       "      <td>5.534653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ffeef32299d913d6</th>\n",
-       "      <td>{CVE-2008-1592, CVE-2009-0439, CVE-2008-1130}</td>\n",
-       "      <td>[2008-03-31, 2009-02-24, 2008-03-04]</td>\n",
+       "      <td>{CVE-2009-0439, CVE-2008-1592, CVE-2008-1130}</td>\n",
+       "      <td>[2009-02-24, 2008-03-31, 2008-03-04]</td>\n",
        "      <td>2008-03-04</td>\n",
        "      <td>7.2</td>\n",
+       "      <td>6.133333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>a092aebf5a286ded</th>\n",
@@ -214,6 +229,7 @@
        "      <td>[2004-12-31]</td>\n",
        "      <td>2004-12-31</td>\n",
        "      <td>7.5</td>\n",
+       "      <td>7.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ace069b9b7c10f19</th>\n",
@@ -221,60 +237,62 @@
        "      <td>[2000-10-20]</td>\n",
        "      <td>2000-10-20</td>\n",
        "      <td>7.5</td>\n",
+       "      <td>7.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5da939327a14e438</th>\n",
-       "      <td>{CVE-2009-2500, CVE-2007-0030, CVE-2015-2520, ...</td>\n",
-       "      <td>[2009-10-14, 2007-01-09, 2015-09-09, 2009-02-2...</td>\n",
-       "      <td>2006-06-17</td>\n",
+       "      <td>{CVE-2006-0028, CVE-2006-0030, CVE-2006-1301, ...</td>\n",
+       "      <td>[2006-03-14, 2006-03-14, 2006-07-13, 2007-01-0...</td>\n",
+       "      <td>1999-05-07</td>\n",
        "      <td>9.3</td>\n",
+       "      <td>7.653333</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>589 rows × 4 columns</p>\n",
+       "<p>616 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                                                       related_cves  \\\n",
-       "c4bd865f2ae51533                                    {CVE-2019-5408}   \n",
-       "ebd276cca70fd723                     {CVE-2019-4513, CVE-2017-1732}   \n",
-       "7a53ce3f91bf73c7  {CVE-2017-14800, CVE-2017-9276, CVE-2017-14799...   \n",
-       "b2aea954a265af60  {CVE-2021-44168, CVE-2021-42757, CVE-2021-2610...   \n",
-       "28819ed8f96586bc  {CVE-2021-44168, CVE-2021-42757, CVE-2021-2610...   \n",
+       "ebd276cca70fd723  {CVE-2013-5421, CVE-2017-1732, CVE-2013-5420, ...   \n",
+       "7a53ce3f91bf73c7  {CVE-2017-14800, CVE-2017-14802, CVE-2017-1480...   \n",
+       "28819ed8f96586bc  {CVE-2021-41032, CVE-2021-43206, CVE-2021-2610...   \n",
+       "ac5e56e41a0b950e  {CVE-2019-17655, CVE-2021-43081, CVE-2019-1765...   \n",
+       "1f061d2b3f2e51d1  {CVE-2018-13374, CVE-2018-13376, CVE-2017-1418...   \n",
        "...                                                             ...   \n",
-       "5f1df5ad8e51ba75  {CVE-2007-0411, CVE-2005-4704, CVE-2006-0427, ...   \n",
-       "ffeef32299d913d6      {CVE-2008-1592, CVE-2009-0439, CVE-2008-1130}   \n",
+       "5f1df5ad8e51ba75  {CVE-2003-1221, CVE-2006-2469, CVE-2006-0430, ...   \n",
+       "ffeef32299d913d6      {CVE-2009-0439, CVE-2008-1592, CVE-2008-1130}   \n",
        "a092aebf5a286ded                                    {CVE-2004-2558}   \n",
        "ace069b9b7c10f19                                    {CVE-2000-0772}   \n",
-       "5da939327a14e438  {CVE-2009-2500, CVE-2007-0030, CVE-2015-2520, ...   \n",
+       "5da939327a14e438  {CVE-2006-0028, CVE-2006-0030, CVE-2006-1301, ...   \n",
        "\n",
        "                                                cve_published_dates  \\\n",
-       "c4bd865f2ae51533                                       [2019-08-09]   \n",
-       "ebd276cca70fd723                           [2019-08-26, 2018-08-17]   \n",
-       "7a53ce3f91bf73c7  [2018-03-01, 2018-03-02, 2018-03-01, 2018-03-0...   \n",
-       "b2aea954a265af60  [2022-01-04, 2021-12-08, 2021-12-08, 2021-08-0...   \n",
-       "28819ed8f96586bc  [2022-01-04, 2021-12-08, 2021-12-08, 2021-08-0...   \n",
+       "ebd276cca70fd723  [2013-12-22, 2018-08-17, 2013-12-23, 2013-12-2...   \n",
+       "7a53ce3f91bf73c7  [2018-03-01, 2018-03-02, 2018-03-02, 2018-03-0...   \n",
+       "28819ed8f96586bc  [2022-05-04, 2022-05-04, 2021-12-08, 2021-12-0...   \n",
+       "ac5e56e41a0b950e  [2020-06-16, 2022-05-11, 2021-04-12, 2022-03-0...   \n",
+       "1f061d2b3f2e51d1  [2019-01-22, 2018-11-27, 2018-05-25, 2017-08-1...   \n",
        "...                                                             ...   \n",
-       "5f1df5ad8e51ba75  [2007-01-23, 2005-12-31, 2006-01-25, 2007-01-2...   \n",
-       "ffeef32299d913d6               [2008-03-31, 2009-02-24, 2008-03-04]   \n",
+       "5f1df5ad8e51ba75  [2003-12-31, 2006-05-19, 2006-01-25, 2004-04-1...   \n",
+       "ffeef32299d913d6               [2009-02-24, 2008-03-31, 2008-03-04]   \n",
        "a092aebf5a286ded                                       [2004-12-31]   \n",
        "ace069b9b7c10f19                                       [2000-10-20]   \n",
-       "5da939327a14e438  [2009-10-14, 2007-01-09, 2015-09-09, 2009-02-2...   \n",
+       "5da939327a14e438  [2006-03-14, 2006-03-14, 2006-07-13, 2007-01-0...   \n",
        "\n",
-       "                 earliest_cve  worst_cve  \n",
-       "c4bd865f2ae51533   2019-08-09        6.5  \n",
-       "ebd276cca70fd723   2018-08-17        8.2  \n",
-       "7a53ce3f91bf73c7   2018-03-01        6.1  \n",
-       "b2aea954a265af60   2020-09-24        9.8  \n",
-       "28819ed8f96586bc   2020-09-24        9.8  \n",
-       "...                       ...        ...  \n",
-       "5f1df5ad8e51ba75   2003-08-27       10.0  \n",
-       "ffeef32299d913d6   2008-03-04        7.2  \n",
-       "a092aebf5a286ded   2004-12-31        7.5  \n",
-       "ace069b9b7c10f19   2000-10-20        7.5  \n",
-       "5da939327a14e438   2006-06-17        9.3  \n",
+       "                 earliest_cve  worst_cve_score  avg_cve_score  \n",
+       "ebd276cca70fd723   2013-12-22             10.0       5.800000  \n",
+       "7a53ce3f91bf73c7   2018-03-01              6.1       6.100000  \n",
+       "28819ed8f96586bc   2020-09-24              9.8       6.866667  \n",
+       "ac5e56e41a0b950e   2019-08-23              9.8       7.019048  \n",
+       "1f061d2b3f2e51d1   2017-08-10              9.8       6.778000  \n",
+       "...                       ...              ...            ...  \n",
+       "5f1df5ad8e51ba75   2003-08-27             10.0       5.534653  \n",
+       "ffeef32299d913d6   2008-03-04              7.2       6.133333  \n",
+       "a092aebf5a286ded   2004-12-31              7.5       7.500000  \n",
+       "ace069b9b7c10f19   2000-10-20              7.5       7.500000  \n",
+       "5da939327a14e438   1999-05-07              9.3       7.653333  \n",
        "\n",
-       "[589 rows x 4 columns]"
+       "[616 rows x 5 columns]"
       ]
      },
      "execution_count": 4,
@@ -284,7 +302,7 @@
    ],
    "source": [
     "# Take a look at columns related to CVEs\n",
-    "df.loc[~df.related_cves.isna(), ['related_cves', 'cve_published_dates', 'earliest_cve', 'worst_cve']]"
+    "df.loc[~df.related_cves.isna(), ['related_cves', 'cve_published_dates', 'earliest_cve', 'worst_cve_score', 'avg_cve_score']]"
    ]
   },
   {
@@ -296,23 +314,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'Categories (without smartcards) comparison between CPE-rich, CVE-rich and all certificates'}>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -322,7 +330,7 @@
     }
    ],
    "source": [
-    "df_cpe_rich = df.loc[~df.verified_cpe_matches.isnull()].copy()\n",
+    "df_cpe_rich = df.loc[~df.cpe_matches.isnull()].copy()\n",
     "df_cve_rich = df.loc[df.n_cves > 0].copy()\n",
     "\n",
     "categories_cpe = df_cpe_rich.category.value_counts().sort_index().rename('Category distribution CPE-rich')\n",
@@ -331,7 +339,9 @@
     "\n",
     "categories_merged = pd.concat([categories_all, categories_cpe, categories_cve], axis=1)\n",
     "categories_merged = categories_merged.div(categories_merged.sum(axis=0), axis=1)\n",
-    "categories_merged.plot.bar(title='Categories (without smartcards) comparison between CPE-rich, CVE-rich and all certificates')"
+    "\n",
+    "categories_merged.plot.bar(title='Categories (without smartcards) comparison between CPE-rich, CVE-rich and all certificates')\n",
+    "plt.savefig(RESULTS_DIR / \"categories.pdf\", bbox_inches='tight')"
    ]
   },
   {
@@ -343,23 +353,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'Years comparision between CPE-rich, CVE-rich and all certificates'}>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -375,7 +375,8 @@
     "\n",
     "years_merged = pd.concat([years_all, years_cpe, years_cve], axis=1)\n",
     "years_merged = years_merged.div(years_merged.sum(axis=0), axis=1)\n",
-    "years_merged.plot.line(title='Years comparision between CPE-rich, CVE-rich and all certificates')"
+    "years_merged.plot.line(title='Years comparision between CPE-rich, CVE-rich and all certificates')\n",
+    "plt.savefig(RESULTS_DIR / \"cve_cpe_certs_time_evolution.pdf\", bbox_inches='tight')"
    ]
   },
   {
@@ -387,23 +388,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'Highest security level comparision between CPE-rich, CVE-rich and all certificates'}>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -413,13 +404,14 @@
     }
    ],
    "source": [
-    "levels_cpe = df_cpe_rich.highest_security_level.value_counts().sort_index().rename('Highest security level distribution CPE-rich')\n",
-    "levels_cve = df_cve_rich.highest_security_level.value_counts().sort_index().rename('Highest security level distribution CVE-rich')\n",
-    "levels_all = df.highest_security_level.value_counts().sort_index().rename('Highest security level distribution all certificates')\n",
+    "levels_cpe = df_cpe_rich.eal.value_counts().sort_index().rename('EAL distribution CPE-rich')\n",
+    "levels_cve = df_cve_rich.eal.value_counts().sort_index().rename('EAL distribution CVE-rich')\n",
+    "levels_all = df.eal.value_counts().sort_index().rename('EAL distribution all certificates')\n",
     "\n",
     "levels_merged = pd.concat([levels_all, levels_cpe, levels_cve], axis=1)\n",
     "levels_merged = levels_merged.div(levels_merged.sum(axis=0), axis=1)\n",
-    "levels_merged.plot.bar(title='Highest security level comparision between CPE-rich, CVE-rich and all certificates')"
+    "levels_merged.plot.bar(title='EAL comparision between CPE-rich, CVE-rich and all certificates')\n",
+    "plt.savefig(RESULTS_DIR / \"eal_distr_all_cpes_cves.pdf\", bbox_inches='tight')"
    ]
   },
   {
@@ -431,23 +423,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'Common vendors comparison between CPE-rich, CVE-rich and all certificates'}>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -464,7 +446,8 @@
     "\n",
     "vendors_merged = pd.concat([vendors_all, vendors_cpe, vendors_cve], axis=1)\n",
     "vendors_merged = vendors_merged.div(vendors_merged.sum(axis=0), axis=1)\n",
-    "vendors_merged.plot.bar(title='Common vendors comparison between CPE-rich, CVE-rich and all certificates')"
+    "vendors_merged.plot.bar(title='Common vendors comparison between CPE-rich, CVE-rich and all certificates')\n",
+    "plt.savefig(RESULTS_DIR / \"top_vendors_vulns.pdf\", bbox_inches='tight')"
    ]
   },
   {
@@ -476,18 +459,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b73343955af4ffa9dd01000907d9864",
+       "model_id": "39296b50def9436f852cd4b0ccc3a6e6",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/135 [00:00<?, ?it/s]"
+       "  0%|          | 0/152 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -506,12 +489,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e9745e0a8f38415197bd3143cc902a41",
+       "model_id": "14ca832ec5844b729167fc910012b2bf",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/135 [00:00<?, ?it/s]"
+       "  0%|          | 0/152 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -519,13 +502,13 @@
     }
    ],
    "source": [
-    "df_corr_all = compute_cve_correlations(df, exclude_vuln_free_certs = False, output_path=\"/Users/adam/phd/projects/certificates/sec-certs/data/sar_correlations/all_certs_sar_cve_corr.csv\")\n",
-    "df_corr_vuln_rich = compute_cve_correlations(df, exclude_vuln_free_certs = True, output_path=\"/Users/adam/phd/projects/certificates/sec-certs/data/sar_correlations/vuln_rich_certs_sar_cve_corr.csv\")"
+    "df_corr_all = compute_cve_correlations(df, exclude_vuln_free_certs = False, output_path= RESULTS_DIR / \"./all_certs_sar_cve_corr.csv\")\n",
+    "df_corr_vuln_rich = compute_cve_correlations(df, exclude_vuln_free_certs = True, output_path= RESULTS_DIR / \"./vuln_rich_certs_sar_cve_corr.csv\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -550,55 +533,61 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>n_cves_corr</th>\n",
-       "      <th>worst_cve_corr</th>\n",
+       "      <th>worst_cve_score_corr</th>\n",
+       "      <th>avg_cve_score_corr</th>\n",
        "      <th>support</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>EAL</th>\n",
-       "      <td>-0.090801</td>\n",
-       "      <td>-0.122635</td>\n",
-       "      <td>4965</td>\n",
+       "      <th>eal</th>\n",
+       "      <td>-0.088312</td>\n",
+       "      <td>-0.200293</td>\n",
+       "      <td>-0.149932</td>\n",
+       "      <td>4892</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ADV_IMP</th>\n",
-       "      <td>-0.067373</td>\n",
-       "      <td>-0.011090</td>\n",
-       "      <td>2075</td>\n",
+       "      <th>ADV_FSP</th>\n",
+       "      <td>-0.062971</td>\n",
+       "      <td>-0.189728</td>\n",
+       "      <td>-0.088043</td>\n",
+       "      <td>4508</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ALC_TAT</th>\n",
-       "      <td>-0.095240</td>\n",
-       "      <td>-0.282163</td>\n",
-       "      <td>1903</td>\n",
+       "      <th>ATE_IND</th>\n",
+       "      <td>-0.083833</td>\n",
+       "      <td>-0.177343</td>\n",
+       "      <td>-0.141519</td>\n",
+       "      <td>4318</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ALC_DVS</th>\n",
-       "      <td>-0.064516</td>\n",
-       "      <td>-0.320521</td>\n",
-       "      <td>2803</td>\n",
+       "      <th>ATE_FUN</th>\n",
+       "      <td>0.026353</td>\n",
+       "      <td>-0.112258</td>\n",
+       "      <td>-0.080423</td>\n",
+       "      <td>3602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ACM_SCP</th>\n",
-       "      <td>-0.043073</td>\n",
-       "      <td>0.058181</td>\n",
-       "      <td>773</td>\n",
+       "      <th>ATE_COV</th>\n",
+       "      <td>0.026174</td>\n",
+       "      <td>-0.046710</td>\n",
+       "      <td>-0.068880</td>\n",
+       "      <td>3573</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         n_cves_corr  worst_cve_corr  support\n",
-       "EAL        -0.090801       -0.122635     4965\n",
-       "ADV_IMP    -0.067373       -0.011090     2075\n",
-       "ALC_TAT    -0.095240       -0.282163     1903\n",
-       "ALC_DVS    -0.064516       -0.320521     2803\n",
-       "ACM_SCP    -0.043073        0.058181      773"
+       "         n_cves_corr  worst_cve_score_corr  avg_cve_score_corr  support\n",
+       "eal        -0.088312             -0.200293           -0.149932     4892\n",
+       "ADV_FSP    -0.062971             -0.189728           -0.088043     4508\n",
+       "ATE_IND    -0.083833             -0.177343           -0.141519     4318\n",
+       "ATE_FUN     0.026353             -0.112258           -0.080423     3602\n",
+       "ATE_COV     0.026174             -0.046710           -0.068880     3573"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -610,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -635,55 +624,61 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>n_cves_corr</th>\n",
-       "      <th>worst_cve_corr</th>\n",
+       "      <th>worst_cve_score_corr</th>\n",
+       "      <th>avg_cve_score_corr</th>\n",
        "      <th>support</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>EAL</th>\n",
-       "      <td>-0.126538</td>\n",
-       "      <td>-0.122635</td>\n",
-       "      <td>589</td>\n",
+       "      <th>eal</th>\n",
+       "      <td>-0.123749</td>\n",
+       "      <td>-0.200293</td>\n",
+       "      <td>-0.149932</td>\n",
+       "      <td>616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ADV_IMP</th>\n",
-       "      <td>-0.023401</td>\n",
-       "      <td>-0.011090</td>\n",
-       "      <td>151</td>\n",
+       "      <th>ADV_FSP</th>\n",
+       "      <td>-0.061666</td>\n",
+       "      <td>-0.189728</td>\n",
+       "      <td>-0.088043</td>\n",
+       "      <td>593</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ALC_TAT</th>\n",
-       "      <td>-0.110212</td>\n",
-       "      <td>-0.282163</td>\n",
-       "      <td>147</td>\n",
+       "      <th>ATE_IND</th>\n",
+       "      <td>-0.126763</td>\n",
+       "      <td>-0.177343</td>\n",
+       "      <td>-0.141519</td>\n",
+       "      <td>589</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ALC_DVS</th>\n",
-       "      <td>-0.033864</td>\n",
-       "      <td>-0.320521</td>\n",
-       "      <td>205</td>\n",
+       "      <th>ALC_CMS</th>\n",
+       "      <td>-0.101118</td>\n",
+       "      <td>-0.268558</td>\n",
+       "      <td>-0.150241</td>\n",
+       "      <td>435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ACM_SCP</th>\n",
-       "      <td>-0.098861</td>\n",
-       "      <td>0.058181</td>\n",
-       "      <td>103</td>\n",
+       "      <th>AVA_VAN</th>\n",
+       "      <td>-0.092757</td>\n",
+       "      <td>-0.336588</td>\n",
+       "      <td>-0.178806</td>\n",
+       "      <td>434</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         n_cves_corr  worst_cve_corr  support\n",
-       "EAL        -0.126538       -0.122635      589\n",
-       "ADV_IMP    -0.023401       -0.011090      151\n",
-       "ALC_TAT    -0.110212       -0.282163      147\n",
-       "ALC_DVS    -0.033864       -0.320521      205\n",
-       "ACM_SCP    -0.098861        0.058181      103"
+       "         n_cves_corr  worst_cve_score_corr  avg_cve_score_corr  support\n",
+       "eal        -0.123749             -0.200293           -0.149932      616\n",
+       "ADV_FSP    -0.061666             -0.189728           -0.088043      593\n",
+       "ATE_IND    -0.126763             -0.177343           -0.141519      589\n",
+       "ALC_CMS    -0.101118             -0.268558           -0.150241      435\n",
+       "AVA_VAN    -0.092757             -0.336588           -0.178806      434"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -709,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -717,14 +712,14 @@
      "output_type": "stream",
      "text": [
       "Number of certificates with >0 CVEs but 0 maintenance reports: 0\n",
-      "Number of certificates with >0 CVEs and >0 maintenance reports: 27\n",
-      "count                             23\n",
-      "mean     857 days 22:57:23.478260864\n",
-      "std      860 days 23:37:09.219855856\n",
-      "min                 34 days 00:00:00\n",
-      "25%                272 days 00:00:00\n",
-      "50%                541 days 00:00:00\n",
-      "75%                962 days 12:00:00\n",
+      "Number of certificates with >0 CVEs and >0 maintenance reports: 35\n",
+      "count                             28\n",
+      "mean     919 days 22:17:08.571428576\n",
+      "std      794 days 10:10:48.864775512\n",
+      "min                  1 days 00:00:00\n",
+      "25%                438 days 06:00:00\n",
+      "50%                622 days 00:00:00\n",
+      "75%               1149 days 06:00:00\n",
       "max               3752 days 00:00:00\n",
       "Name: time_to_fix_cve, dtype: object\n"
      ]
@@ -738,7 +733,599 @@
     "\n",
     "df_fixed = compute_maintenances_that_should_fix_vulns(df)\n",
     "print(df_fixed.loc[~df_fixed.time_to_fix_cve.isnull()]['time_to_fix_cve'].describe())\n",
-    "mu_filenames = move_fixing_mu_to_directory(df_fixed, main_dset.to_pandas(), \"/Users/adam/Downloads/mu\", \"/Users/adam/phd/projects/certificates/sec-certs/datasets/from_web_latest/certs/maintenances/reports/pdf\")"
+    "updates_that_should_fix_vulns_path = RESULTS_DIR / \"updates_that_should_fix_vulns\"\n",
+    "updates_that_should_fix_vulns_path.mkdir(exist_ok=True)\n",
+    "mu_filenames = move_fixing_mu_to_directory(df_fixed, main_dset.to_pandas(), updates_that_should_fix_vulns_path, RESULTS_DIR / \"certs/maintenances/reports/pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Histogram timeline of new vulnerabilities\n",
+    "\n",
+    "Shows when vulnerabilities are announced in relation to the date of certification & date of certificate expiration.\n",
+    "\n",
+    "*Note*: Some certificates (especially the new ones) don't have their expiration date set yet. These are discarded from the analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xlink:href=\"#ArialMT-30\" x=\"114.013672\"/>\n       <use xlink:href=\"#ArialMT-30\" x=\"169.628906\"/>\n       <use xlink:href=\"#ArialMT-30\" x=\"225.244141\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_12\">\n     <g id=\"line2d_44\">\n      <path d=\"M 620.177574 247.570312 \nL 620.177574 21.935625 \n\" clip-path=\"url(#p139220e137)\" style=\"fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round\"/>\n     </g>\n     <g id=\"line2d_45\"/>\n     <g id=\"text_25\">\n      <!-- −2000 -->\n      <g style=\"fill: #262626\" transform=\"translate(606.135386 258.228125)scale(0.1 -0.1)\">\n       <use xlink:href=\"#ArialMT-2212\"/>\n       <use xlink:href=\"#ArialMT-32\" x=\"58.398438\"/>\n       <use xlink:href=\"#ArialMT-30\" x=\"114.013672\"/>\n       <use xlink:href=\"#ArialMT-30\" x=\"169.628906\"/>\n       <use xlink:href=\"#ArialMT-30\" x=\"225.244141\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_13\">\n     <g id=\"line2d_46\">\n      <path d=\"M 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+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio of CVEs appearing before (or exactly on) certification date: 26.54%\n",
+      "Ratio of CVEs appearing after certification date: 73.46%\n"
+     ]
+    }
+   ],
+   "source": [
+    "exploded_cves = df_cve_rich.explode(\"related_cves\").loc[:, [\"related_cves\", \"not_valid_before\", \"not_valid_after\"]].rename(columns={\"related_cves\": \"cve\"})\n",
+    "exploded_cves = exploded_cves.dropna() # <-- Investigate why we have 1056 empty not_valid_after records\n",
+    "exploded_cves[\"cve_published_date\"] = exploded_cves.cve.map(lambda x: cve_dset[x].published_date)\n",
+    "\n",
+    "exploded_cves.not_valid_before = exploded_cves.not_valid_before.dt.normalize()\n",
+    "exploded_cves.not_valid_after = exploded_cves.not_valid_after.dt.normalize()\n",
+    "exploded_cves.cve_published_date = exploded_cves.cve_published_date.dt.tz_localize(None).dt.normalize()\n",
+    "\n",
+    "exploded_cves[\"n_days_after_certification\"] = (exploded_cves.cve_published_date - exploded_cves.not_valid_before).dt.days\n",
+    "exploded_cves[\"n_days_after_expiry\"] = (exploded_cves.cve_published_date - exploded_cves.not_valid_after).dt.days\n",
+    "\n",
+    "plt.rcParams[\"figure.figsize\"] = [12, 4]\n",
+    "plt.rcParams[\"figure.autolayout\"] = True\n",
+    "figure, axes = plt.subplots(1, 2)\n",
+    "\n",
+    "hist = sns.histplot(exploded_cves.n_days_after_certification, kde=True, ax=axes[0])\n",
+    "hist.set(xlim=(-4000,4600), title=\"CVEs appearing n days after certification\", xlabel=\"Number of days after date of certification\", ylabel=\"Frequency of CVEs\")\n",
+    "hist.axvline(0, color=\"red\", linewidth=\"1\", label=\"Day of certification\")\n",
+    "hist.legend(loc=\"upper right\")\n",
+    "# plt.savefig(RESULTS_DIR / \"cves_n_days_after_certification.pdf\", bbox_inches='tight')\n",
+    "# plt.show()\n",
+    "\n",
+    "hist = sns.histplot(exploded_cves.n_days_after_expiry, kde=True, ax=axes[1])\n",
+    "hist.set(xlim=(-6200, 4000), title=\"CVEs appearing n days after certificate expiry date\", xlabel=\"Number of days after certificate expiration\", ylabel=\"Frequency of CVEs\")\n",
+    "hist.axvline(0, color=\"red\", linewidth=\"1\", label=\"Day of expiration\")\n",
+    "hist.legend(loc=\"upper left\")\n",
+    "plt.savefig(RESULTS_DIR / \"cves_vs_certificate_lifetime.pdf\", bbox_inches='tight')\n",
+    "plt.show()\n",
+    "\n",
+    "n_cves = exploded_cves.shape[0]\n",
+    "ratio_before_cert = exploded_cves.loc[exploded_cves.n_days_after_certification <= 0].shape[0] / n_cves\n",
+    "ratio_after_cert = exploded_cves.loc[exploded_cves.n_days_after_certification > 0].shape[0] / n_cves\n",
+    "print(f\"Ratio of CVEs appearing before (or exactly on) certification date: {100 * ratio_before_cert:.2f}%\")\n",
+    "print(f\"Ratio of CVEs appearing after certification date: {100 * ratio_after_cert:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How many CVE-rich certificates were revoked within <365 days after certification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "How many CVE-rich certificates were revoked in <365 days after certification: 3\n",
+      "How many certificates were revoked in <365 days after certification: 39\n"
+     ]
+    }
+   ],
+   "source": [
+    "maybe_revoked = df_cve_rich.loc[~df_cve_rich.not_valid_after.isna(), [\"not_valid_before\", \"not_valid_after\", \"n_cves\", \"worst_cve_score\", \"avg_cve_score\", \"related_cves\"]]\n",
+    "maybe_revoked[\"validity_n_days\"] = (maybe_revoked.not_valid_after - maybe_revoked.not_valid_before).dt.days\n",
+    "maybe_revoked = maybe_revoked.loc[maybe_revoked.validity_n_days < 365]\n",
+    "print(f'How many CVE-rich certificates were revoked in <365 days after certification: {maybe_revoked.shape[0]}')\n",
+    "\n",
+    "df_w_validity_dates = df.loc[~df.not_valid_after.isna()].copy()\n",
+    "df_w_validity_dates.loc[:, \"validity_n_days\"] = (df_w_validity_dates.not_valid_after - df_w_validity_dates.not_valid_before).dt.days\n",
+    "df_w_validity_dates = df_w_validity_dates.loc[df_w_validity_dates.validity_n_days < 365]\n",
+    "print(f'How many certificates were revoked in <365 days after certification: {df_w_validity_dates.shape[0]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>not_valid_before</th>\n",
+       "      <th>not_valid_after</th>\n",
+       "      <th>validity_n_days</th>\n",
+       "      <th>related_cves</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>602f65f51a07adaf</th>\n",
+       "      <td>Brocade Communications Systems, Inc. Brocade D...</td>\n",
+       "      <td>2015-03-25</td>\n",
+       "      <td>2016-03-21</td>\n",
+       "      <td>362</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a7120da6bf4874fa</th>\n",
+       "      <td>DigiSAFE Data Diode model 3282 version 2.2, mo...</td>\n",
+       "      <td>2019-06-17</td>\n",
+       "      <td>2020-06-10</td>\n",
+       "      <td>359</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e2f3a1cdd592ab05</th>\n",
+       "      <td>Cisco 800, 1900, 2900, 3900 Series Integrated ...</td>\n",
+       "      <td>2011-07-31</td>\n",
+       "      <td>2012-07-20</td>\n",
+       "      <td>355</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f410930a2a47d3d0</th>\n",
+       "      <td>Avaya VSP 4000, VSP 7000 and VSP 8000</td>\n",
+       "      <td>2017-03-10</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>353</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16bdbde359584f99</th>\n",
+       "      <td>Xaica-α PLUS ePassport on MTCOS Pro 2.5 with S...</td>\n",
+       "      <td>2019-01-25</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>312</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0c7ef6c32cbdee47</th>\n",
+       "      <td>Xaica-α PLUS ePassport on MTCOS Pro 2.5 with S...</td>\n",
+       "      <td>2019-01-25</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>312</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3c24bbe7724dbfdd</th>\n",
+       "      <td>McAfee® Email Gateway (MEG) software v7.0.1, r...</td>\n",
+       "      <td>2012-10-16</td>\n",
+       "      <td>2013-08-21</td>\n",
+       "      <td>309</td>\n",
+       "      <td>{CVE-2016-8005, CVE-2012-4581, CVE-2012-4585, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9e0ec9db58b5c8ec</th>\n",
+       "      <td>RICOH MP C306Z J-1.01</td>\n",
+       "      <td>2016-04-25</td>\n",
+       "      <td>2017-02-13</td>\n",
+       "      <td>294</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5068c3b7fefae589</th>\n",
+       "      <td>bizhub C652 / bizhub C652DS / bizhub C552 / bi...</td>\n",
+       "      <td>2010-06-29</td>\n",
+       "      <td>2011-04-15</td>\n",
+       "      <td>290</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25fcafd838cecd84</th>\n",
+       "      <td>bizhub C360 / bizhub C280 / bizhub C220 / bizh...</td>\n",
+       "      <td>2010-06-29</td>\n",
+       "      <td>2011-04-15</td>\n",
+       "      <td>290</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2748f920f4df8eaf</th>\n",
+       "      <td>IDeal Pass v2 - SAC/EAC JC ePassport 4.0.0</td>\n",
+       "      <td>2013-12-20</td>\n",
+       "      <td>2014-09-09</td>\n",
+       "      <td>263</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13e306eb69c8bf95</th>\n",
+       "      <td>ALE Omniswitch 6250, 6350, and 6450 with the A...</td>\n",
+       "      <td>2017-10-17</td>\n",
+       "      <td>2018-06-27</td>\n",
+       "      <td>253</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a0176ceabc4bfc0a</th>\n",
+       "      <td>Cisco Expressway X12.5</td>\n",
+       "      <td>2019-08-19</td>\n",
+       "      <td>2020-02-26</td>\n",
+       "      <td>191</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fd31c2a2bbfcc4a9</th>\n",
+       "      <td>Pro 8100/8110/8120 (Ricoh/Savin/Lanier/nashuat...</td>\n",
+       "      <td>2016-08-10</td>\n",
+       "      <td>2017-02-13</td>\n",
+       "      <td>187</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5a22fab9376989ef</th>\n",
+       "      <td>Cisco Email Security Appliance v9.1</td>\n",
+       "      <td>2015-09-18</td>\n",
+       "      <td>2016-03-21</td>\n",
+       "      <td>185</td>\n",
+       "      <td>{CVE-2014-0739, CVE-2020-3181, CVE-2013-5542, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7f1129858dd504a3</th>\n",
+       "      <td>FortiSwitch blade appliances with FortiTRNG ru...</td>\n",
+       "      <td>2014-11-07</td>\n",
+       "      <td>2014-12-22</td>\n",
+       "      <td>45</td>\n",
+       "      <td>{CVE-2018-13374, CVE-2018-13376, CVE-2013-4604...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124272e23ec6ac09</th>\n",
+       "      <td>Samsung Galaxy Note 7 MDFPP</td>\n",
+       "      <td>2016-10-14</td>\n",
+       "      <td>2016-11-14</td>\n",
+       "      <td>31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a606e94c8e0e3028</th>\n",
+       "      <td>Samsung Galaxy Note 7 VPNCPP</td>\n",
+       "      <td>2016-10-17</td>\n",
+       "      <td>2016-11-14</td>\n",
+       "      <td>28</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dc7ac9298e12f7ee</th>\n",
+       "      <td>NPCT7xx TPM2.0 rev1.38 Hardware version LAG019...</td>\n",
+       "      <td>2019-01-18</td>\n",
+       "      <td>2019-01-18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3746512864b941d4</th>\n",
+       "      <td>NPCT6xx TPM 2.0 Hardware version FB5C85D and F...</td>\n",
+       "      <td>2017-09-25</td>\n",
+       "      <td>2017-09-25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22ede463dbf1a105</th>\n",
+       "      <td>ST33TPHF20I2CTPM Firmware version 74.05</td>\n",
+       "      <td>2017-07-19</td>\n",
+       "      <td>2017-07-19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4f953ae00a24eb85</th>\n",
+       "      <td>ST33TPHF2EI2C mode TPM 2.0TPM Firmware version...</td>\n",
+       "      <td>2017-07-19</td>\n",
+       "      <td>2017-07-19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0bf7a19b22163465</th>\n",
+       "      <td>ST33TPHF20SPI TPM Firmware version 74.00</td>\n",
+       "      <td>2016-12-13</td>\n",
+       "      <td>2016-12-13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a5898123ec124b34</th>\n",
+       "      <td>ST33TPHF2ESPI mode TPM 2.0 TPM Firmware versio...</td>\n",
+       "      <td>2016-12-13</td>\n",
+       "      <td>2016-12-13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4e7b5a210809acf9</th>\n",
+       "      <td>TPM 2.0 Hardware version FB5C85D, Firmware ver...</td>\n",
+       "      <td>2016-07-22</td>\n",
+       "      <td>2016-07-22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158950ed4bc35274</th>\n",
+       "      <td>ST33TPHF2ESPI mode TPM 2.0 TPM Firmware versio...</td>\n",
+       "      <td>2016-07-04</td>\n",
+       "      <td>2016-07-04</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>eff64d1800f26807</th>\n",
+       "      <td>NPCT7xx TPM 2.0 Hardware version LAG019, Firmw...</td>\n",
+       "      <td>2018-01-19</td>\n",
+       "      <td>2018-01-19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66838f2e4bb6eccc</th>\n",
+       "      <td>ST33TPHF20 TPM Firmware versions 74.08 (0x4A 0...</td>\n",
+       "      <td>2018-09-24</td>\n",
+       "      <td>2018-09-24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d4647f5217d84ba3</th>\n",
+       "      <td>CELES-c002 Machine Readable Electronic Documen...</td>\n",
+       "      <td>2020-11-02</td>\n",
+       "      <td>2020-11-02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a334142059d865a2</th>\n",
+       "      <td>NPCT7xx TPM1.2 rev 116; Hardware version LAG01...</td>\n",
+       "      <td>2019-08-14</td>\n",
+       "      <td>2019-08-14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e2f5592cc95676e7</th>\n",
+       "      <td>Huawei FusionDirector 1.5.1.SPC1</td>\n",
+       "      <td>2020-10-23</td>\n",
+       "      <td>2020-10-23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23a85a7f9c07412f</th>\n",
+       "      <td>Microcontrôleurs sécurisés ST33G1M2A et ST33G1...</td>\n",
+       "      <td>2017-02-16</td>\n",
+       "      <td>2017-02-16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17b5257b3755a07c</th>\n",
+       "      <td>Plate-forme STSAFE-J, en configuration fermée,...</td>\n",
+       "      <td>2017-03-23</td>\n",
+       "      <td>2017-03-23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e27937cf16c20f8f</th>\n",
+       "      <td>ST33JM2M0 B02 including optional cryptographic...</td>\n",
+       "      <td>2017-09-05</td>\n",
+       "      <td>2017-09-05</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10b17081dd7cad8f</th>\n",
+       "      <td>ST33TPHF2E mode TPM 2.0 TPM Firmware versions ...</td>\n",
+       "      <td>2018-09-24</td>\n",
+       "      <td>2018-09-24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58ece75f7f2b5099</th>\n",
+       "      <td>J-SIGN version 1.8.9</td>\n",
+       "      <td>2019-01-11</td>\n",
+       "      <td>2019-01-11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2731d8ddba404fad</th>\n",
+       "      <td>JSAFE3_EPASS EAC (Rev. I)</td>\n",
+       "      <td>2019-04-09</td>\n",
+       "      <td>2019-04-09</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f34df0dd7011366e</th>\n",
+       "      <td>JSAFE3_EPASS BAC (Rev. M)</td>\n",
+       "      <td>2019-04-09</td>\n",
+       "      <td>2019-04-09</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bad93fb821395db2</th>\n",
+       "      <td>ST33TPHF20SPI</td>\n",
+       "      <td>2016-05-30</td>\n",
+       "      <td>2016-05-30</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                               name  \\\n",
+       "602f65f51a07adaf  Brocade Communications Systems, Inc. Brocade D...   \n",
+       "a7120da6bf4874fa  DigiSAFE Data Diode model 3282 version 2.2, mo...   \n",
+       "e2f3a1cdd592ab05  Cisco 800, 1900, 2900, 3900 Series Integrated ...   \n",
+       "f410930a2a47d3d0              Avaya VSP 4000, VSP 7000 and VSP 8000   \n",
+       "16bdbde359584f99  Xaica-α PLUS ePassport on MTCOS Pro 2.5 with S...   \n",
+       "0c7ef6c32cbdee47  Xaica-α PLUS ePassport on MTCOS Pro 2.5 with S...   \n",
+       "3c24bbe7724dbfdd  McAfee® Email Gateway (MEG) software v7.0.1, r...   \n",
+       "9e0ec9db58b5c8ec                              RICOH MP C306Z J-1.01   \n",
+       "5068c3b7fefae589  bizhub C652 / bizhub C652DS / bizhub C552 / bi...   \n",
+       "25fcafd838cecd84  bizhub C360 / bizhub C280 / bizhub C220 / bizh...   \n",
+       "2748f920f4df8eaf         IDeal Pass v2 - SAC/EAC JC ePassport 4.0.0   \n",
+       "13e306eb69c8bf95  ALE Omniswitch 6250, 6350, and 6450 with the A...   \n",
+       "a0176ceabc4bfc0a                             Cisco Expressway X12.5   \n",
+       "fd31c2a2bbfcc4a9  Pro 8100/8110/8120 (Ricoh/Savin/Lanier/nashuat...   \n",
+       "5a22fab9376989ef                Cisco Email Security Appliance v9.1   \n",
+       "7f1129858dd504a3  FortiSwitch blade appliances with FortiTRNG ru...   \n",
+       "124272e23ec6ac09                        Samsung Galaxy Note 7 MDFPP   \n",
+       "a606e94c8e0e3028                       Samsung Galaxy Note 7 VPNCPP   \n",
+       "dc7ac9298e12f7ee  NPCT7xx TPM2.0 rev1.38 Hardware version LAG019...   \n",
+       "3746512864b941d4  NPCT6xx TPM 2.0 Hardware version FB5C85D and F...   \n",
+       "22ede463dbf1a105            ST33TPHF20I2CTPM Firmware version 74.05   \n",
+       "4f953ae00a24eb85  ST33TPHF2EI2C mode TPM 2.0TPM Firmware version...   \n",
+       "0bf7a19b22163465           ST33TPHF20SPI TPM Firmware version 74.00   \n",
+       "a5898123ec124b34  ST33TPHF2ESPI mode TPM 2.0 TPM Firmware versio...   \n",
+       "4e7b5a210809acf9  TPM 2.0 Hardware version FB5C85D, Firmware ver...   \n",
+       "158950ed4bc35274  ST33TPHF2ESPI mode TPM 2.0 TPM Firmware versio...   \n",
+       "eff64d1800f26807  NPCT7xx TPM 2.0 Hardware version LAG019, Firmw...   \n",
+       "66838f2e4bb6eccc  ST33TPHF20 TPM Firmware versions 74.08 (0x4A 0...   \n",
+       "d4647f5217d84ba3  CELES-c002 Machine Readable Electronic Documen...   \n",
+       "a334142059d865a2  NPCT7xx TPM1.2 rev 116; Hardware version LAG01...   \n",
+       "e2f5592cc95676e7                   Huawei FusionDirector 1.5.1.SPC1   \n",
+       "23a85a7f9c07412f  Microcontrôleurs sécurisés ST33G1M2A et ST33G1...   \n",
+       "17b5257b3755a07c  Plate-forme STSAFE-J, en configuration fermée,...   \n",
+       "e27937cf16c20f8f  ST33JM2M0 B02 including optional cryptographic...   \n",
+       "10b17081dd7cad8f  ST33TPHF2E mode TPM 2.0 TPM Firmware versions ...   \n",
+       "58ece75f7f2b5099                               J-SIGN version 1.8.9   \n",
+       "2731d8ddba404fad                          JSAFE3_EPASS EAC (Rev. I)   \n",
+       "f34df0dd7011366e                          JSAFE3_EPASS BAC (Rev. M)   \n",
+       "bad93fb821395db2                                      ST33TPHF20SPI   \n",
+       "\n",
+       "                 not_valid_before not_valid_after  validity_n_days  \\\n",
+       "602f65f51a07adaf       2015-03-25      2016-03-21              362   \n",
+       "a7120da6bf4874fa       2019-06-17      2020-06-10              359   \n",
+       "e2f3a1cdd592ab05       2011-07-31      2012-07-20              355   \n",
+       "f410930a2a47d3d0       2017-03-10      2018-02-26              353   \n",
+       "16bdbde359584f99       2019-01-25      2019-12-03              312   \n",
+       "0c7ef6c32cbdee47       2019-01-25      2019-12-03              312   \n",
+       "3c24bbe7724dbfdd       2012-10-16      2013-08-21              309   \n",
+       "9e0ec9db58b5c8ec       2016-04-25      2017-02-13              294   \n",
+       "5068c3b7fefae589       2010-06-29      2011-04-15              290   \n",
+       "25fcafd838cecd84       2010-06-29      2011-04-15              290   \n",
+       "2748f920f4df8eaf       2013-12-20      2014-09-09              263   \n",
+       "13e306eb69c8bf95       2017-10-17      2018-06-27              253   \n",
+       "a0176ceabc4bfc0a       2019-08-19      2020-02-26              191   \n",
+       "fd31c2a2bbfcc4a9       2016-08-10      2017-02-13              187   \n",
+       "5a22fab9376989ef       2015-09-18      2016-03-21              185   \n",
+       "7f1129858dd504a3       2014-11-07      2014-12-22               45   \n",
+       "124272e23ec6ac09       2016-10-14      2016-11-14               31   \n",
+       "a606e94c8e0e3028       2016-10-17      2016-11-14               28   \n",
+       "dc7ac9298e12f7ee       2019-01-18      2019-01-18                0   \n",
+       "3746512864b941d4       2017-09-25      2017-09-25                0   \n",
+       "22ede463dbf1a105       2017-07-19      2017-07-19                0   \n",
+       "4f953ae00a24eb85       2017-07-19      2017-07-19                0   \n",
+       "0bf7a19b22163465       2016-12-13      2016-12-13                0   \n",
+       "a5898123ec124b34       2016-12-13      2016-12-13                0   \n",
+       "4e7b5a210809acf9       2016-07-22      2016-07-22                0   \n",
+       "158950ed4bc35274       2016-07-04      2016-07-04                0   \n",
+       "eff64d1800f26807       2018-01-19      2018-01-19                0   \n",
+       "66838f2e4bb6eccc       2018-09-24      2018-09-24                0   \n",
+       "d4647f5217d84ba3       2020-11-02      2020-11-02                0   \n",
+       "a334142059d865a2       2019-08-14      2019-08-14                0   \n",
+       "e2f5592cc95676e7       2020-10-23      2020-10-23                0   \n",
+       "23a85a7f9c07412f       2017-02-16      2017-02-16                0   \n",
+       "17b5257b3755a07c       2017-03-23      2017-03-23                0   \n",
+       "e27937cf16c20f8f       2017-09-05      2017-09-05                0   \n",
+       "10b17081dd7cad8f       2018-09-24      2018-09-24                0   \n",
+       "58ece75f7f2b5099       2019-01-11      2019-01-11                0   \n",
+       "2731d8ddba404fad       2019-04-09      2019-04-09                0   \n",
+       "f34df0dd7011366e       2019-04-09      2019-04-09                0   \n",
+       "bad93fb821395db2       2016-05-30      2016-05-30                0   \n",
+       "\n",
+       "                                                       related_cves  \n",
+       "602f65f51a07adaf                                                NaN  \n",
+       "a7120da6bf4874fa                                                NaN  \n",
+       "e2f3a1cdd592ab05                                                NaN  \n",
+       "f410930a2a47d3d0                                                NaN  \n",
+       "16bdbde359584f99                                                NaN  \n",
+       "0c7ef6c32cbdee47                                                NaN  \n",
+       "3c24bbe7724dbfdd  {CVE-2016-8005, CVE-2012-4581, CVE-2012-4585, ...  \n",
+       "9e0ec9db58b5c8ec                                                NaN  \n",
+       "5068c3b7fefae589                                                NaN  \n",
+       "25fcafd838cecd84                                                NaN  \n",
+       "2748f920f4df8eaf                                                NaN  \n",
+       "13e306eb69c8bf95                                                NaN  \n",
+       "a0176ceabc4bfc0a                                                NaN  \n",
+       "fd31c2a2bbfcc4a9                                                NaN  \n",
+       "5a22fab9376989ef  {CVE-2014-0739, CVE-2020-3181, CVE-2013-5542, ...  \n",
+       "7f1129858dd504a3  {CVE-2018-13374, CVE-2018-13376, CVE-2013-4604...  \n",
+       "124272e23ec6ac09                                                NaN  \n",
+       "a606e94c8e0e3028                                                NaN  \n",
+       "dc7ac9298e12f7ee                                                NaN  \n",
+       "3746512864b941d4                                                NaN  \n",
+       "22ede463dbf1a105                                                NaN  \n",
+       "4f953ae00a24eb85                                                NaN  \n",
+       "0bf7a19b22163465                                                NaN  \n",
+       "a5898123ec124b34                                                NaN  \n",
+       "4e7b5a210809acf9                                                NaN  \n",
+       "158950ed4bc35274                                                NaN  \n",
+       "eff64d1800f26807                                                NaN  \n",
+       "66838f2e4bb6eccc                                                NaN  \n",
+       "d4647f5217d84ba3                                                NaN  \n",
+       "a334142059d865a2                                                NaN  \n",
+       "e2f5592cc95676e7                                                NaN  \n",
+       "23a85a7f9c07412f                                                NaN  \n",
+       "17b5257b3755a07c                                                NaN  \n",
+       "e27937cf16c20f8f                                                NaN  \n",
+       "10b17081dd7cad8f                                                NaN  \n",
+       "58ece75f7f2b5099                                                NaN  \n",
+       "2731d8ddba404fad                                                NaN  \n",
+       "f34df0dd7011366e                                                NaN  \n",
+       "bad93fb821395db2                                                NaN  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_w_validity_dates.loc[:, [\"name\",  \"not_valid_before\", \"not_valid_after\", \"validity_n_days\", \"related_cves\"]].sort_values(by=\"validity_n_days\", ascending=False)"
    ]
   }
  ],
diff --git a/notebooks/examples/common_criteria.ipynb b/notebooks/examples/common_criteria.ipynb
new file mode 100644
index 00000000..677dcd28
--- /dev/null
+++ b/notebooks/examples/common_criteria.ipynb
@@ -0,0 +1,252 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common Criteria example\n",
+    "\n",
+    "This notebook illustrates basic functionality with the `CCDataset` class that holds Common Criteria dataset and of its sample `CommonCriteriaCert`.\n",
+    "\n",
+    "Note that there exists a front end to this functionality at [seccerts.org/cc](https://seccerts.org/cc/). Before reinventing the wheel, it's good idea to check our web. Maybe you don't even need to run the code, but just use our web instead. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sec_certs.dataset.common_criteria import CCDataset\n",
+    "from sec_certs.dataset.common_criteria import CommonCriteriaCert\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get fresh dataset snapshot from mirror\n",
+    "\n",
+    "There's no need to do full processing of the dataset by yourself, unless you modified `sec-certs` code. You can simply fetch the processed version from the web. \n",
+    "\n",
+    "Note, however, that you won't be able to access the `pdf` and `txt` files of the certificates. You can only get the data that we extracted from it. \n",
+    "\n",
+    "Running the whole pipeline can get you the `pdf` and `txt` data. You can see how to do that in the last cell of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dset = CCDataset.from_web_latest()\n",
+    "print(len(dset)) # Print number of certificates in the dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Do some basic dataset serialization\n",
+    "\n",
+    "The dataset can be saved/loaded into/from `json`. Also, the dataset can be converted into a [pandas](https://pandas.pydata.org/) DataFrame. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dump dataset into json and load it back\n",
+    "dset.to_json(\"./cc_dset.json\")\n",
+    "new_dset: CCDataset = CCDataset.from_json(\"./cc_dset.json\")\n",
+    "assert dset == new_dset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Turn dataset into Pandas DataFrame\n",
+    "df = dset.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple dataset manipulation\n",
+    "\n",
+    "The certificates of the dataset are stored in a dictionary that maps certificate's primary key (we call it `dgst`) to the `CommonCriteriaCert` object. The primary key of the certificate is simply a hash of the attributes that make the certificate unique.\n",
+    "\n",
+    "You can iterate over the dataset which is handy when selecting some subset of certificates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Iterate over certificates in dataset\n",
+    "for cert in dset:\n",
+    "    pass\n",
+    "\n",
+    "# Get certificates produced by Infineon manufacturer\n",
+    "infineon_certs = [x for x in dset if \"Infineon\" in x.manufacturer]\n",
+    "df_infineon = df.loc[df.manufacturer.str.contains(\"Infineon\", case=False)]\n",
+    "\n",
+    "# Get certificates with some CVE\n",
+    "vulnerable_certs = [x for x in dset if x.heuristics.related_cves]\n",
+    "df_vulnerable = df.loc[~df.related_cves.isna()]\n",
+    "\n",
+    "# Show CVE ids of some vulnerable certificate\n",
+    "print(f\"{vulnerable_certs[0].heuristics.related_cves=}\")\n",
+    "\n",
+    "# Get certificates from 2015 and newer\n",
+    "df_2015_and_newer = df.loc[df.year_from > 2014]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot distribution of years of certification\n",
+    "df.year_from.value_counts().sort_index().plot.line()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dissect single certificate\n",
+    "\n",
+    "The `CommonCriteriaCert` is basically a data structure that holds all the data we keep about a certificate. Other classes (`CCDataset` or `model` package members) are used to transform and process the certificates. You can see all its attributes at [API docs](https://seccerts.org/docs/api/sample.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select a certificate and print some attributes\n",
+    "cert: CommonCriteriaCert = dset[\"bad93fb821395db2\"]\n",
+    "print(f\"{cert.name=}\")\n",
+    "print(f\"{cert.heuristics.cpe_matches=}\")\n",
+    "print(f\"{cert.heuristics.report_references.directly_referencing=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select all certificates from a dataset for which we detect at least one vulnerability.\n",
+    "vulnerable_certs = [x for x in dset if x.heuristics.related_cves]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Serialize single certificate\n",
+    "\n",
+    "Again, a certificate can be (de)serialized into/from json. It's also possible to construct pandas `Series` from a certificate as shown below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cert.to_json(\"./cert.json\")\n",
+    "new_cert = cert.from_json(\"./cert.json\")\n",
+    "assert cert == new_cert\n",
+    "\n",
+    "# Serialize as Pandas series\n",
+    "ser = pd.Series(cert.pandas_tuple, index=cert.pandas_columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assign dataset with CPE records and compute vulnerabilities\n",
+    "\n",
+    "*Note*: The data is already computed on dataset obtained with `from_web_latest()`, this is just for illustration. \n",
+    "*Note*: This may likely not run in Binder, as the corresponding `CVEDataset` and `CPEDataset` instances take a lot of memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Automatically match CPEs and CVEs\n",
+    "_, cpe_dset, _ = dset.compute_cpe_heuristics()\n",
+    "dset.compute_related_cves()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create new dataset and fully process it\n",
+    "\n",
+    "The following piece of code roughly corresponds to `$ cc-certs all` CLI command -- it fully processes the CC pipeline. This will create a folder in current working directory where the outputs will be stored. \n",
+    "\n",
+    "*Warning*: It's not good idea to run this from notebook. It may take several hours to finnish. We recommend using `from_web_latest()` or turning this into a Python script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dset = CCDataset()\n",
+    "dset.get_certs_from_web()\n",
+    "dset.process_protection_profiles()\n",
+    "dset.download_all_pdfs()\n",
+    "dset.convert_all_pdfs()\n",
+    "dset.analyze_certificates()"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "6386d1612879d92d026c363e7667e428bc38d86c5a080d58c3d70e7cd43df67d"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.1 ('certsvenv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/examples/fips.ipynb b/notebooks/examples/fips.ipynb
new file mode 100644
index 00000000..28eb2b1a
--- /dev/null
+++ b/notebooks/examples/fips.ipynb
@@ -0,0 +1,178 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FIPS Dataset class\n",
+    "\n",
+    "This notebook illustrates basic functionality with the `FIPSDataset` class that holds FIPS 140 dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sec_certs.dataset.fips import FIPSDataset\n",
+    "from sec_certs.sample import FIPSCertificate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get fresh dataset snapshot from mirror"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dset: FIPSDataset = FIPSDataset.from_web_latest()\n",
+    "print(len(dset))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Do some basic dataset serialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dump dataset into json and load it back\n",
+    "dset.to_json(\"./fips_dataset.json\")\n",
+    "new_dset = FIPSDataset.from_json(\"./fips_dataset.json\")\n",
+    "assert dset == new_dset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple dataset manipulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get certificates from a single manufacturer\n",
+    "cisco_certs = [cert for cert in dset if \"Cisco\" in cert.web_scan.vendor]\n",
+    "\n",
+    "# Get certificates with some CVE\n",
+    "vulnerable_certs = [cert for cert in dset if cert.heuristics.related_cves]\n",
+    "\n",
+    "# Show CVE ids of some vulnerable certificate\n",
+    "print(f\"{vulnerable_certs[0].heuristics.related_cves=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dissect single certificate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select a certificate and print some attributes\n",
+    "cert: FIPSCertificate = dset[\"542cacae1d41132a\"]\n",
+    "\n",
+    "print(f\"{cert.web_scan.module_name=}\")\n",
+    "print(f\"{cert.heuristics.cpe_matches=}\")\n",
+    "print(f\"{cert.web_scan.level=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Serialize single certificate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cert.to_json(\"./cert.json\")\n",
+    "new_cert: FIPSCertificate = FIPSCertificate.from_json(\"./cert.json\")\n",
+    "assert new_cert == cert"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create new dataset and fully process it\n",
+    "\n",
+    "*Warning*: It's not good idea to run this from notebook. It may take several hours to finnish. We recommend using `from_web_latest()` or turning this into a Python script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dset = FIPSDataset()\n",
+    "dset.get_certs_from_web()\n",
+    "dset.convert_all_pdfs()\n",
+    "dset.pdf_scan()\n",
+    "dset.extract_certs_from_tables()\n",
+    "dset.finalize_results()\n",
+    "\n",
+    "# TODO: Resolve https://github.com/crocs-muni/sec-certs/issues/210 and refactor this\n",
+    "# if not no_download_algs:\n",
+    "#     aset.get_certs_from_web()\n",
+    "#     logger.info(f\"Finished parsing. Have algorithm dataset with {len(aset)} algorithm numbers.\")\n",
+    "#     dset.algorithms = aset\n",
+    "\n",
+    "# TODO: Resolve https://github.com/crocs-muni/sec-certs/issues/211 and uncomment\n",
+    "# dset.plot_graphs(show=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "6386d1612879d92d026c363e7667e428bc38d86c5a080d58c3d70e7cd43df67d"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.1 ('certsvenv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/examples/model.ipynb b/notebooks/examples/model.ipynb
new file mode 100644
index 00000000..370b13cd
--- /dev/null
+++ b/notebooks/examples/model.ipynb
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model\n",
+    "\n",
+    "This notebook illustrates basic functionality with the `model` package that apply complex transformations on certificates.\n",
+    "\n",
+    "*Note*: You probably don't need to use this. Instead, you should use `CCDataset` or `FIPSDataset` classes to handle the transformations for yourself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sec_certs.dataset.common_criteria import CCDataset\n",
+    "from sec_certs.model import SARTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dset: CCDataset = CCDataset.from_web_latest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SARTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformer = SARTransformer().fit(dset.certs.values())\n",
+    "extracted_sars = {x.dgst: transformer.transform_single_cert(x) for x in dset}"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "6386d1612879d92d026c363e7667e428bc38d86c5a080d58c3d70e7cd43df67d"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.1 ('certsvenv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 7ac509d6..6bae9df8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,9 @@
+[build-system]
+requires = ["setuptools>=45", "setuptools_scm[toml]>=6.2"]
+
+[tool.setuptools_scm]
+write_to = "sec_certs/_version.py"
+
 [tool.black]
 line-length = 120
 exclude = '''
@@ -16,21 +22,19 @@ exclude = '''
 '''
 
 [tool.isort]
-multi_line_output=3
-include_trailing_comma=true
-force_grid_wrap=0
-use_parentheses=true
-ensure_newline_before_comments=true
-line_length=120
-skip=["certsvenv", "build"]
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+ensure_newline_before_comments = true
+line_length = 120
+skip = ["certsvenv", "build"]
 
 [tool.mypy]
 plugins = ["numpy.typing.mypy_plugin"]
 ignore_missing_imports = true
-exclude="build/"
+exclude = "build/"
 
 [tool.pytest.ini_options]
-markers = [
-    "slow: marks tests as slow (deselect with '-m \"not slow\"')"
-]
+markers = ["slow: marks tests as slow (deselect with '-m \"not slow\"')"]
 addopts = "--cov sec_certs"
diff --git a/requirements/dev_requirements.in b/requirements/dev_requirements.in
index 54bba20d..7f6d9712 100644
--- a/requirements/dev_requirements.in
+++ b/requirements/dev_requirements.in
@@ -10,4 +10,9 @@ black
 isort
 flake8
 pre-commit
-pip-tools
\ No newline at end of file
+pip-tools
+sphinx
+myst-nb>=0.14
+sphinx-book-theme
+sphinx-design
+sphinx-copybutton
\ No newline at end of file
diff --git a/requirements/dev_requirements.txt b/requirements/dev_requirements.txt
index 1658784c..3ea4c3af 100644
--- a/requirements/dev_requirements.txt
+++ b/requirements/dev_requirements.txt
@@ -1,5 +1,23 @@
+alabaster==0.7.12
+    # via sphinx
+appnope==0.1.3
+    # via
+    #   ipykernel
+    #   ipython
+asttokens==2.0.5
+    # via stack-data
 attrs==21.4.0
-    # via pytest
+    # via
+    #   jsonschema
+    #   jupyter-cache
+    #   markdown-it-py
+    #   pytest
+babel==2.10.1
+    # via sphinx
+backcall==0.2.0
+    # via ipython
+beautifulsoup4==4.11.1
+    # via pydata-sphinx-theme
 black==22.3.0
     # via -r dev_requirements.in
 certifi==2021.10.8
@@ -11,27 +29,92 @@ charset-normalizer==2.0.12
 click==8.1.2
     # via
     #   black
+    #   jupyter-cache
     #   pip-tools
 coverage[toml]==6.3.2
     # via pytest-cov
+debugpy==1.6.0
+    # via ipykernel
+decorator==5.1.1
+    # via ipython
 distlib==0.3.4
     # via virtualenv
+docutils==0.17.1
+    # via
+    #   myst-nb
+    #   myst-parser
+    #   pydata-sphinx-theme
+    #   sphinx
+    #   sphinx-togglebutton
+entrypoints==0.4
+    # via jupyter-client
+executing==0.8.3
+    # via stack-data
+fastjsonschema==2.15.3
+    # via nbformat
 filelock==3.6.0
     # via virtualenv
 flake8==4.0.1
     # via -r dev_requirements.in
 gprof2dot==2021.2.21
     # via pytest-profiling
+greenlet==1.1.2
+    # via sqlalchemy
 identify==2.4.12
     # via pre-commit
 idna==3.3
     # via requests
+imagesize==1.3.0
+    # via sphinx
+importlib-metadata==4.11.3
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   sphinx
+importlib-resources==5.7.1
+    # via jsonschema
 iniconfig==1.1.1
     # via pytest
+ipykernel==6.13.0
+    # via myst-nb
+ipython==8.2.0
+    # via
+    #   ipykernel
+    #   myst-nb
 isort==5.10.1
     # via -r dev_requirements.in
+jedi==0.18.1
+    # via ipython
+jinja2==3.1.1
+    # via
+    #   myst-parser
+    #   sphinx
+jsonschema==4.4.0
+    # via nbformat
+jupyter-cache==0.5.0
+    # via myst-nb
+jupyter-client==7.2.2
+    # via
+    #   ipykernel
+    #   nbclient
+jupyter-core==4.10.0
+    # via
+    #   jupyter-client
+    #   nbformat
+markdown-it-py==1.1.0
+    # via
+    #   mdit-py-plugins
+    #   myst-parser
+markupsafe==2.1.1
+    # via jinja2
+matplotlib-inline==0.1.3
+    # via
+    #   ipykernel
+    #   ipython
 mccabe==0.6.1
     # via flake8
+mdit-py-plugins==0.3.0
+    # via myst-parser
 memory-profiler==0.60.0
     # via pytest-monitor
 mypy==0.942
@@ -40,14 +123,40 @@ mypy-extensions==0.4.3
     # via
     #   black
     #   mypy
+myst-nb==0.14.0
+    # via -r dev_requirements.in
+myst-parser==0.17.2
+    # via myst-nb
+nbclient==0.5.13
+    # via jupyter-cache
+nbformat==5.3.0
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   nbclient
+nest-asyncio==1.5.5
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   nbclient
 nodeenv==1.6.0
     # via pre-commit
 packaging==21.3
-    # via pytest
+    # via
+    #   ipykernel
+    #   pydata-sphinx-theme
+    #   pytest
+    #   sphinx
+parso==0.8.3
+    # via jedi
 pathspec==0.9.0
     # via black
 pep517==0.12.0
     # via pip-tools
+pexpect==4.8.0
+    # via ipython
+pickleshare==0.7.5
+    # via ipython
 pip-tools==6.5.1
     # via -r dev_requirements.in
 platformdirs==2.5.1
@@ -58,18 +167,33 @@ pluggy==1.0.0
     # via pytest
 pre-commit==2.18.1
     # via -r dev_requirements.in
+prompt-toolkit==3.0.29
+    # via ipython
 psutil==5.9.0
     # via
+    #   ipykernel
     #   memory-profiler
     #   pytest-monitor
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.2
+    # via stack-data
 py==1.11.0
     # via pytest
 pycodestyle==2.8.0
     # via flake8
+pydata-sphinx-theme==0.8.1
+    # via sphinx-book-theme
 pyflakes==2.4.0
     # via flake8
+pygments==2.12.0
+    # via
+    #   ipython
+    #   sphinx
 pyparsing==3.0.7
     # via packaging
+pyrsistent==0.18.1
+    # via jsonschema
 pytest==7.1.1
     # via
     #   -r dev_requirements.in
@@ -82,14 +206,69 @@ pytest-monitor==1.6.3
     # via -r dev_requirements.in
 pytest-profiling==1.7.0
     # via -r dev_requirements.in
+python-dateutil==2.8.2
+    # via jupyter-client
+pytz==2022.1
+    # via babel
 pyyaml==6.0
-    # via pre-commit
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   myst-parser
+    #   pre-commit
+    #   sphinx-book-theme
+pyzmq==22.3.0
+    # via jupyter-client
 requests==2.27.1
-    # via pytest-monitor
+    # via
+    #   pytest-monitor
+    #   sphinx
 six==1.16.0
     # via
+    #   asttokens
     #   pytest-profiling
+    #   python-dateutil
     #   virtualenv
+snowballstemmer==2.2.0
+    # via sphinx
+soupsieve==2.3.2.post1
+    # via beautifulsoup4
+sphinx==4.5.0
+    # via
+    #   -r dev_requirements.in
+    #   myst-nb
+    #   myst-parser
+    #   pydata-sphinx-theme
+    #   sphinx-book-theme
+    #   sphinx-copybutton
+    #   sphinx-design
+    #   sphinx-togglebutton
+sphinx-book-theme==0.3.2
+    # via -r dev_requirements.in
+sphinx-copybutton==0.5.0
+    # via -r dev_requirements.in
+sphinx-design==0.1.0
+    # via -r dev_requirements.in
+sphinx-togglebutton==0.3.1
+    # via myst-nb
+sphinxcontrib-applehelp==1.0.2
+    # via sphinx
+sphinxcontrib-devhelp==1.0.2
+    # via sphinx
+sphinxcontrib-htmlhelp==2.0.0
+    # via sphinx
+sphinxcontrib-jsmath==1.0.1
+    # via sphinx
+sphinxcontrib-qthelp==1.0.3
+    # via sphinx
+sphinxcontrib-serializinghtml==1.1.5
+    # via sphinx
+sqlalchemy==1.4.35
+    # via jupyter-cache
+stack-data==0.2.0
+    # via ipython
+tabulate==0.8.9
+    # via jupyter-cache
 toml==0.10.2
     # via pre-commit
 tomli==2.0.1
@@ -99,6 +278,19 @@ tomli==2.0.1
     #   mypy
     #   pep517
     #   pytest
+tornado==6.1
+    # via
+    #   ipykernel
+    #   jupyter-client
+traitlets==5.1.1
+    # via
+    #   ipykernel
+    #   ipython
+    #   jupyter-client
+    #   jupyter-core
+    #   matplotlib-inline
+    #   nbclient
+    #   nbformat
 types-python-dateutil==2.8.10
     # via -r dev_requirements.in
 types-pyyaml==6.0.5
@@ -111,14 +303,23 @@ typing-extensions==4.1.1
     # via
     #   black
     #   mypy
+    #   myst-nb
+    #   myst-parser
 urllib3==1.26.9
     # via requests
 virtualenv==20.14.0
     # via pre-commit
+wcwidth==0.2.5
+    # via prompt-toolkit
 wheel==0.37.1
     # via
     #   pip-tools
     #   pytest-monitor
+    #   sphinx-togglebutton
+zipp==3.8.0
+    # via
+    #   importlib-metadata
+    #   importlib-resources
 
 # The following packages are considered to be unsafe in a requirements file:
 # pip
diff --git a/requirements/requirements.in b/requirements/requirements.in
index 27e14075..e677f203 100644
--- a/requirements/requirements.in
+++ b/requirements/requirements.in
@@ -19,6 +19,9 @@ requests
 scikit-learn
 tabula-py
 tqdm
+setuptools-scm
 ipykernel
 ipywidgets
 spacy
+pkgconfig
+seaborn
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index cb8923c3..18f67fb3 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,3 +1,7 @@
+appnope==0.1.3
+    # via
+    #   ipykernel
+    #   ipython
 argon2-cffi==21.3.0
     # via notebook
 argon2-cffi-bindings==21.2.0
@@ -66,6 +70,8 @@ html5lib==1.1
     # via -r requirements.in
 idna==3.3
     # via requests
+importlib-resources==5.7.1
+    # via jsonschema
 ipykernel==6.12.1
     # via
     #   -r requirements.in
@@ -124,7 +130,9 @@ markupsafe==2.1.1
     #   jinja2
     #   nbconvert
 matplotlib==3.5.1
-    # via -r requirements.in
+    # via
+    #   -r requirements.in
+    #   seaborn
 matplotlib-inline==0.1.3
     # via
     #   ipykernel
@@ -162,6 +170,7 @@ numpy==1.22.3
     #   pandas
     #   scikit-learn
     #   scipy
+    #   seaborn
     #   spacy
     #   tabula-py
     #   thinc
@@ -171,10 +180,12 @@ packaging==21.3
     #   matplotlib
     #   nbconvert
     #   pikepdf
+    #   setuptools-scm
     #   spacy
 pandas==1.4.2
     # via
     #   -r requirements.in
+    #   seaborn
     #   tabula-py
 pandocfilters==1.5.0
     # via nbconvert
@@ -195,6 +206,8 @@ pillow==9.1.0
     #   -r requirements.in
     #   matplotlib
     #   pikepdf
+pkgconfig==1.5.5
+    # via -r requirements.in
 preshed==3.0.6
     # via
     #   spacy
@@ -252,9 +265,15 @@ requests==2.27.1
 scikit-learn==1.0.2
     # via -r requirements.in
 scipy==1.8.0
-    # via scikit-learn
+    # via
+    #   scikit-learn
+    #   seaborn
+seaborn==0.11.2
+    # via -r requirements.in
 send2trash==1.8.0
     # via notebook
+setuptools-scm==6.4.2
+    # via -r requirements.in
 six==1.16.0
     # via
     #   asttokens
@@ -287,6 +306,8 @@ threadpoolctl==3.1.0
     # via scikit-learn
 tinycss2==1.1.1
     # via nbconvert
+tomli==2.0.1
+    # via setuptools-scm
 tornado==6.1
     # via
     #   ipykernel
@@ -331,6 +352,8 @@ webencodings==0.5.1
     #   tinycss2
 widgetsnbextension==3.6.0
     # via ipywidgets
+zipp==3.8.0
+    # via importlib-resources
 
 # The following packages are considered to be unsafe in a requirements file:
 # setuptools
diff --git a/sec_certs/config/configuration.py b/sec_certs/config/configuration.py
index 8560529c..6a9a5880 100644
--- a/sec_certs/config/configuration.py
+++ b/sec_certs/config/configuration.py
@@ -1,6 +1,6 @@
 import json
 from pathlib import Path
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 import jsonschema
 import yaml
@@ -30,6 +30,12 @@ class Configuration(object):
             return res["value"]
         return object.__getattribute__(self, key)
 
+    def get_desription(self, key: str) -> Optional[str]:
+        res = object.__getattribute__(self, key)
+        if isinstance(res, dict) and "description" in res:
+            return res["description"]
+        return None
+
 
 DEFAULT_CONFIG_PATH = Path(__file__).parent / "settings.yaml"
 config = Configuration()
diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py
index 7bdb5ae1..8ea091db 100644
--- a/sec_certs/dataset/common_criteria.py
+++ b/sec_certs/dataset/common_criteria.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import copy
 import itertools
 import json
@@ -28,6 +30,11 @@ from sec_certs.serialization.json import ComplexSerializableType, CustomJSONDeco
 
 
 class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
+    """
+    Class that holds CommonCriteriaCert. Serializable into json, pandas, dictionary. Conveys basic certificate manipulations
+    and dataset transformations. Many private methods that perform internal operations, feel free to exploit them.
+    """
+
     @dataclass
     class DatasetInternalState(ComplexSerializableType):
         meta_sources_parsed: bool = False
@@ -56,9 +63,15 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         return copy.deepcopy(self)
 
     def to_dict(self) -> Dict[str, Any]:
+        """
+        Returns self serialized into dictionary
+        """
         return {**{"state": self.state}, **super().to_dict()}
 
     def to_pandas(self) -> pd.DataFrame:
+        """
+        Return self serialized into pandas DataFrame
+        """
         df = pd.DataFrame([x.pandas_tuple for x in self.certs.values()], columns=CommonCriteriaCert.pandas_columns)
         df = df.set_index("dgst")
 
@@ -79,7 +92,10 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         return df
 
     @classmethod
-    def from_dict(cls: Type["CCDataset"], dct: Dict[str, Any]) -> "CCDataset":
+    def from_dict(cls: Type[CCDataset], dct: Dict[str, Any]) -> "CCDataset":
+        """
+        Reconstructs the CCDataset object from a dictionary
+        """
         dset = super().from_dict(dct)
         dset.state = copy.deepcopy(dct["state"])
         return dset
@@ -89,14 +105,14 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
     def root_dir(self, new_dir: Union[str, Path]) -> None:
         old_dset = copy.deepcopy(self)
         Dataset.root_dir.fset(self, new_dir)  # type: ignore
-        self.set_local_paths()
+        self._set_local_paths()
 
         if self.state and old_dset.root_dir != Path(".."):
             logger.info(f"Changing root dir of partially processed dataset. All contents will get copied to {new_dir}")
-            self.copy_dataset_contents(old_dset)
+            self._copy_dataset_contents(old_dset)
             self.to_json()
 
-    def copy_dataset_contents(self, old_dset: "CCDataset") -> None:
+    def _copy_dataset_contents(self, old_dset: "CCDataset") -> None:
         if old_dset.state.meta_sources_parsed:
             try:
                 shutil.copytree(old_dset.web_dir, self.web_dir)
@@ -115,42 +131,72 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
     @property
     def certs_dir(self) -> Path:
+        """
+        Returns directory that holds files associated with certificates
+        """
         return self.root_dir / "certs"
 
     @property
     def reports_dir(self) -> Path:
+        """
+        Returns directory that holds files associated with certification reports
+        """
         return self.certs_dir / "reports"
 
     @property
     def reports_pdf_dir(self) -> Path:
+        """
+        Returns directory that holds PDFs associated with certification reports
+        """
         return self.reports_dir / "pdf"
 
     @property
     def reports_txt_dir(self) -> Path:
+        """
+        Returns directory that holds TXTs associated with certification reports
+        """
         return self.reports_dir / "txt"
 
     @property
     def targets_dir(self) -> Path:
+        """
+        Returns directory that holds files associated with security targets
+        """
         return self.certs_dir / "targets"
 
     @property
     def targets_pdf_dir(self) -> Path:
+        """
+        Returns directory that holds PDFs associated with security targets
+        """
         return self.targets_dir / "pdf"
 
     @property
     def targets_txt_dir(self) -> Path:
+        """
+        Returns directory that holds TXTs associated with security targets
+        """
         return self.targets_dir / "txt"
 
     @property
     def pp_dataset_path(self) -> Path:
+        """
+        Returns directory that holds files associated with Protection profiles
+        """
         return self.auxillary_datasets_dir / "pp_dataset.json"
 
     @property
     def mu_dataset_path(self) -> Path:
+        """
+        Returns directory that holds dataset of maintenance updates
+        """
         return self.certs_dir / "maintenances"
 
     @property
     def mu_dataset(self) -> "CCDatasetMaintenanceUpdates":
+        """
+        Returns object of dataset of maintenance updates if it exists, raises otherwise
+        """
         if not self.mu_dataset_path.exists():
             raise ValueError("The dataset with maintenance updates does not exist")
 
@@ -176,22 +222,42 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
     @property
     def active_html_tuples(self) -> List[Tuple[str, Path]]:
+        """
+        Returns List Tuple[str, Path] where first element is name of html file and second element is its Path.
+        The files correspond to html files parsed from CC website that list all *active* certificates.
+        """
         return [(x, self.web_dir / y) for y, x in self.HTML_PRODUCTS_URL.items() if "active" in y]
 
     @property
     def archived_html_tuples(self) -> List[Tuple[str, Path]]:
+        """
+        Returns List Tuple[str, Path] where first element is name of html file and second element is its Path.
+        The files correspond to html files parsed from CC website that list all *archived* certificates.
+        """
         return [(x, self.web_dir / y) for y, x in self.HTML_PRODUCTS_URL.items() if "archived" in y]
 
     @property
     def active_csv_tuples(self) -> List[Tuple[str, Path]]:
+        """
+        Returns List Tuple[str, Path] where first element is name of csv file and second element is its Path.
+        The files correspond to csv files downloaded from CC website that list all *active* certificates.
+        """
         return [(x, self.web_dir / y) for y, x in self.CSV_PRODUCTS_URL.items() if "active" in y]
 
     @property
     def archived_csv_tuples(self) -> List[Tuple[str, Path]]:
+        """
+        Returns List Tuple[str, Path] where first element is name of csv file and second element is its Path.
+        The files correspond to csv files downloaded from CC website that list all *archived* certificates.
+        """
         return [(x, self.web_dir / y) for y, x in self.CSV_PRODUCTS_URL.items() if "archived" in y]
 
     @classmethod
     def from_web_latest(cls) -> "CCDataset":
+        """
+        Fetches the fresh snapshot of CCDataset from seccerts.org
+        :return CCDataset: returns the CCDataset object downloaded from seccerts.org
+        """
         with tempfile.TemporaryDirectory() as tmp_dir:
             dset_path = Path(tmp_dir) / "cc_latest_dataset.json"
             helpers.download_file(
@@ -200,7 +266,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
             return cls.from_json(dset_path)
 
     @property
-    def all_cert_ids(self):
+    def _all_cert_ids(self):
         all_cert_ids = set()
 
         for cert_obj in self:
@@ -220,7 +286,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
         return all_cert_ids
 
-    def set_local_paths(self):
+    def _set_local_paths(self):
         for cert in self:
             cert.set_local_paths(self.reports_pdf_dir, self.targets_pdf_dir, self.reports_txt_dir, self.targets_txt_dir)
 
@@ -237,7 +303,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
         logger.info(f"Added {len(new_certs)} new and merged further {len(certs_to_merge)} certificates to the dataset.")
 
-    def download_csv_html_resources(self, get_active: bool = True, get_archived: bool = True) -> None:
+    def _download_csv_html_resources(self, get_active: bool = True, get_archived: bool = True) -> None:
         self.web_dir.mkdir(parents=True, exist_ok=True)
 
         html_items = []
@@ -258,6 +324,14 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
     @serialize
     def process_protection_profiles(self, to_download: bool = True, keep_metadata: bool = True) -> None:
+        """
+        Downloads new snapshot of dataset with processed protection profiles (if it doesn't exist) and links PPs
+        with certificates within self. Assigns PPs to all certificates
+
+        :param bool to_download: If dataset should be downloaded or fetched from json, defaults to True
+        :param bool keep_metadata: If json related to the PP dataset should be kept on drive, defaults to True
+        :raises RuntimeError: When building of PPDataset fails
+        """
         logger.info("Processing protection profiles.")
         constructor: Dict[bool, Callable[..., ProtectionProfileDataset]] = {
             True: ProtectionProfileDataset.from_web,
@@ -280,10 +354,16 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         self, to_download: bool = True, keep_metadata: bool = True, get_active: bool = True, get_archived: bool = True
     ) -> None:
         """
-        Parses all metadata about certificates
+        Downloads CSV and HTML files that hold lists of certificates from common criteria website. Parses these files
+        and constructs CommonCriteriaCert objects, fills the dataset with those.
+
+        :param bool to_download: If CSV and HTML files shall be downloaded (or existing files utilized), defaults to True
+        :param bool keep_metadata: If CSV and HTML files shall be kept on disk after download, defaults to True
+        :param bool get_active: If active certificates shall be parsed, defaults to True
+        :param bool get_archived: If archived certificates shall be parsed, defaults to True
         """
         if to_download is True:
-            self.download_csv_html_resources(get_active, get_archived)
+            self._download_csv_html_resources(get_active, get_archived)
 
         logger.info("Adding CSV certificates to CommonCriteria dataset.")
         csv_certs = self._get_all_certs_from_csv(get_active, get_archived)
@@ -299,7 +379,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         if not keep_metadata:
             shutil.rmtree(self.web_dir)
 
-        self.set_local_paths()
+        self._set_local_paths()
         self.state.meta_sources_parsed = True
 
     def _get_all_certs_from_csv(self, get_active: bool, get_archived: bool) -> Dict[str, "CommonCriteriaCert"]:
@@ -538,7 +618,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         self.targets_pdf_dir.mkdir(parents=True, exist_ok=True)
         certs_to_process = [x for x in self if x.state.report_is_ok_to_download(fresh)]
         cert_processing.process_parallel(
-            CommonCriteriaCert.download_pdf_target,
+            CommonCriteriaCert.download_pdf_st,
             certs_to_process,
             config.n_threads,
             progress_bar_desc="Downloading targets",
@@ -546,6 +626,11 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
     @serialize
     def download_all_pdfs(self, fresh: bool = True) -> None:
+        """
+        Downloads all pdf files associated with certificates of the datset.
+
+        :param bool fresh: whether all (true) or only failed (false) pdfs shall be downloaded, defaults to True
+        """
         if self.state.meta_sources_parsed is False:
             logger.error("Attempting to download pdfs while not having csv/html meta-sources parsed. Returning.")
             return
@@ -579,7 +664,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         self.targets_txt_dir.mkdir(parents=True, exist_ok=True)
         certs_to_process = [x for x in self if x.state.st_is_ok_to_convert(fresh)]
         cert_processing.process_parallel(
-            CommonCriteriaCert.convert_target_pdf,
+            CommonCriteriaCert.convert_st_pdf,
             certs_to_process,
             config.n_threads,
             progress_bar_desc="Converting targets to txt",
@@ -587,6 +672,11 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
     @serialize
     def convert_all_pdfs(self, fresh: bool = True) -> None:
+        """
+        Converts all pdfs associated to certificates of the dataset into txt files.
+
+        :param bool fresh: whether all (true) or only failed (false) pdfs shall be converted, defaults to True
+        """
         if self.state.pdfs_downloaded is False:
             logger.info("Attempting to convert pdf while not having them downloaded. Returning.")
             return
@@ -607,6 +697,11 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         self.state.pdfs_converted = True
 
     def update_with_certs(self, certs: List[CommonCriteriaCert]) -> None:
+        """
+        Enriches the dataset with `certs`
+
+        :param List[CommonCriteriaCert] certs: new certs to include into the dataset.
+        """
         if any([x not in self for x in certs]):
             logger.warning("Updating dataset with certificates outside of the dataset!")
         self.certs.update({x.dgst: x for x in certs})
@@ -633,7 +728,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         )
         self.update_with_certs(processed_certs)
 
-    def extract_pdf_metadata(self, fresh: bool = True) -> None:
+    def _extract_pdf_metadata(self, fresh: bool = True) -> None:
         logger.info("Extracting pdf metadata from CC dataset")
         self._extract_report_metadata(fresh)
         self._extract_targets_metadata(fresh)
@@ -660,7 +755,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         )
         self.update_with_certs(processed_certs)
 
-    def extract_pdf_frontpage(self, fresh: bool = True) -> None:
+    def _extract_pdf_frontpage(self, fresh: bool = True) -> None:
         logger.info("Extracting pdf frontpages from CC dataset.")
         self._extract_report_frontpage(fresh)
         self._extract_targets_frontpage(fresh)
@@ -687,16 +782,16 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         )
         self.update_with_certs(processed_certs)
 
-    def extract_pdf_keywords(self, fresh: bool = True) -> None:
+    def _extract_pdf_keywords(self, fresh: bool = True) -> None:
         logger.info("Extracting pdf keywords from CC dataset.")
         self._extract_report_keywords(fresh)
         self._extract_targets_keywords(fresh)
 
     def _extract_data(self, fresh: bool = True) -> None:
         logger.info("Extracting various stuff from converted txt filed from CC dataset.")
-        self.extract_pdf_metadata(fresh)
-        self.extract_pdf_frontpage(fresh)
-        self.extract_pdf_keywords(fresh)
+        self._extract_pdf_metadata(fresh)
+        self._extract_pdf_frontpage(fresh)
+        self._extract_pdf_keywords(fresh)
 
         if fresh is True:
             logger.info("Attempting to re-extract failed data from report txts")
@@ -718,7 +813,7 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
     def _compute_normalized_cert_ids(self):
         logger.info("Deriving information about sample ids from pdf scan.")
         for cert in self:
-            cert.compute_heuristics_cert_id(self.all_cert_ids)
+            cert.compute_heuristics_cert_id(self._all_cert_ids)
 
     def _compute_dependency_vulnerabilities(self):
         cve_dependency_finder = DependencyVulnerabilityFinder()
@@ -767,6 +862,11 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
     @serialize
     def analyze_certificates(self, fresh: bool = True) -> None:
+        """
+        Searches for all RE in the txt files of the dataset. These are further processed during heuristics computation.
+
+        :param bool fresh: Whether to run this phase on all certificates (true) or only on those that failed (false), defaults to True
+        """
         if self.state.pdfs_converted is False:
             logger.info(
                 "Attempting run analysis of txt files while not having the pdf->txt conversion done. Returning."
@@ -778,10 +878,16 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
 
         self.state.certs_analyzed = True
 
-    def get_certs_from_name(self, cert_name: str) -> List[CommonCriteriaCert]:
+    def _get_certs_from_name(self, cert_name: str) -> List[CommonCriteriaCert]:
         return [crt for crt in self if crt.name == cert_name]
 
-    def process_maintenance_updates(self) -> "CCDatasetMaintenanceUpdates":
+    def process_maintenance_updates(self) -> CCDatasetMaintenanceUpdates:
+        """
+        Creates and fully processes (download, convert, analyze) a dataset of maintenance updates that are related
+        to certificates of self.
+
+        :return CCDatasetMaintenanceUpdates: the resulting dataset of maintenance updates
+        """
         maintained_certs: List[CommonCriteriaCert] = [x for x in self if x.maintenance_updates]
         updates = list(
             itertools.chain.from_iterable(
@@ -791,23 +897,16 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType):
         update_dset: CCDatasetMaintenanceUpdates = CCDatasetMaintenanceUpdates(
             {x.dgst: x for x in updates}, root_dir=self.mu_dataset_path, name="Maintenance updates"
         )
-        update_dset.set_local_paths()
         update_dset.download_all_pdfs()
         update_dset.convert_all_pdfs()
         update_dset._extract_data()
 
         return update_dset
 
-    def generate_cert_name_keywords(self) -> Set[str]:
-        df = self.to_pandas()
-        certificate_names = set(df["name"])
-        keywords = set(itertools.chain.from_iterable([x.lower().split(" ") for x in certificate_names]))
-        keywords.add("1.02.013")
-        return {x for x in keywords if len(x) > config.minimal_token_length}
-
 
 class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType):
     """
+    Dataset of maintenance updates related to certificates of CCDataset dataset.
     Should be used merely for actions related to Maintenance updates: download pdfs, convert pdfs, extract data from pdfs
     """
 
@@ -822,6 +921,7 @@ class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType):
     ):
         super().__init__(certs, root_dir, name, description, state)  # type: ignore
         self.state.meta_sources_parsed = True
+        self._set_local_paths()
 
     @property
     def certs_dir(self) -> Path:
diff --git a/sec_certs/dataset/cpe.py b/sec_certs/dataset/cpe.py
index 43ae3365..0ae35081 100644
--- a/sec_certs/dataset/cpe.py
+++ b/sec_certs/dataset/cpe.py
@@ -20,6 +20,10 @@ logger = logging.getLogger(__name__)
 
 @dataclass
 class CPEDataset(ComplexSerializableType):
+    """
+    Dataset of CPE records. Includes look-up dictionaries for fast search.
+    """
+
     was_enhanced_with_vuln_cpes: bool
     json_path: Path
     cpes: Dict[str, CPE]
@@ -68,7 +72,7 @@ class CPEDataset(ComplexSerializableType):
 
     def build_lookup_dicts(self) -> None:
         """
-        Will build look-up dictionaries that are used for fast matching
+        Will build look-up dictionaries that are used for fast matching.
         """
         logger.info("CPE dataset: building lookup dictionaries.")
         self.vendor_to_versions = {x.vendor: set() for x in self}
@@ -90,6 +94,13 @@ class CPEDataset(ComplexSerializableType):
 
     @classmethod
     def from_web(cls, json_path: Union[str, Path], init_lookup_dicts: bool = True) -> "CPEDataset":
+        """
+        Creates CPEDataset from NIST resources published on-line
+
+        :param Union[str, Path] json_path: Path to store the dataset to
+        :param bool init_lookup_dicts: If dictionaries for fast matching should be computed, defaults to True
+        :return CPEDataset: The resulting dataset
+        """
         with tempfile.TemporaryDirectory() as tmp_dir:
             xml_path = Path(tmp_dir) / cls.cpe_xml_basename
             zip_path = Path(tmp_dir) / (cls.cpe_xml_basename + ".zip")
@@ -98,10 +109,10 @@ class CPEDataset(ComplexSerializableType):
             with zipfile.ZipFile(zip_path, "r") as zip_ref:
                 zip_ref.extractall(tmp_dir)
 
-            return cls.from_xml(xml_path, json_path, init_lookup_dicts)
+            return cls._from_xml(xml_path, json_path, init_lookup_dicts)
 
     @classmethod
-    def from_xml(
+    def _from_xml(
         cls, xml_path: Union[str, Path], json_path: Union[str, Path], init_lookup_dicts: bool = True
     ) -> "CPEDataset":
         logger.info("Loading CPE dataset from XML.")
@@ -128,22 +139,48 @@ class CPEDataset(ComplexSerializableType):
 
     @classmethod
     def from_json(cls, input_path: Union[str, Path]) -> "CPEDataset":
+        """
+        Loads dataset from json
+
+        :param Union[str, Path] input_path: Path to the serialized json dataset
+        :return CPEDataset: the resulting dataset.
+        """
         dset = cast("CPEDataset", ComplexSerializableType.from_json(input_path))
         dset.json_path = Path(input_path)
         return dset
 
     @classmethod
     def from_dict(cls, dct: Dict[str, Any], init_lookup_dicts: bool = True) -> "CPEDataset":
+        """
+        Loads dataset from dictionary.
+
+        :param Dict[str, Any] dct: Dictionary that holds the dataset
+        :param bool init_lookup_dicts: Whether look-up dicts should be computed as a part of initialization, defaults to True
+        :return CPEDataset: the resulting dataset.
+        """
         return cls(dct["was_enhanced_with_vuln_cpes"], Path("../"), dct["cpes"], init_lookup_dicts)
 
     def to_pandas(self) -> pd.DataFrame:
+        """
+        Turns the dataset into pandas DataFrame. Each CPE record forms a row.
+
+        :return pd.DataFrame: the resulting DataFrame
+        """
         df = pd.DataFrame([x.pandas_tuple for x in self], columns=CPE.pandas_columns)
         df = df.set_index("uri")
         return df
 
     @serialize
     def enhance_with_cpes_from_cve_dataset(self, cve_dset: Union[CVEDataset, str, Path]) -> None:
-        def adding_condition(
+        """
+        Some CPEs are present only in the CVEDataset and are missing from the CPE Dataset.
+        This method goes through the provided CVEDataset and enriches self with CPEs from
+        the CVEDataset.
+
+        :param Union[CVEDataset, str, Path] cve_dset: CVEDataset of a path to it.
+        """
+
+        def _adding_condition(
             considered_cpe: CPE,
             vndr_item_lookup: Set[Tuple[str, str]],
             vndr_item_version_lookup: Set[Tuple[str, str, str]],
@@ -174,7 +211,7 @@ class CPEDataset(ComplexSerializableType):
         vendor_item_lookup = {(cpe.vendor, cpe.item_name) for cpe in self}
         vendor_item_version_lookup = {(cpe.vendor, cpe.item_name, cpe.version) for cpe in self}
         for cpe in helpers.tqdm(all_cpes_in_cve_dset, desc="Enriching CPE dataset with new CPEs"):
-            if adding_condition(cpe, vendor_item_lookup, vendor_item_version_lookup):
+            if _adding_condition(cpe, vendor_item_lookup, vendor_item_version_lookup):
                 new_cpe = copy.deepcopy(cpe)
                 new_cpe.start_version = None
                 new_cpe.end_version = None
diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py
index e63aea9f..191c6759 100644
--- a/sec_certs/dataset/dataset.py
+++ b/sec_certs/dataset/dataset.py
@@ -128,10 +128,10 @@ class Dataset(Generic[CertSubType], ABC):
     def from_json(cls: Type[DatasetSubType], input_path: Union[str, Path]) -> DatasetSubType:
         dset = cast("DatasetSubType", ComplexSerializableType.from_json(input_path))
         dset.root_dir = Path(input_path).parent.absolute()
-        dset.set_local_paths()
+        dset._set_local_paths()
         return dset
 
-    def set_local_paths(self) -> None:
+    def _set_local_paths(self) -> None:
         raise NotImplementedError("Not meant to be implemented by the base class.")
 
     @abstractmethod
@@ -289,12 +289,12 @@ class Dataset(Generic[CertSubType], ABC):
             else:
                 cert_name = annotation["text"]
 
-            certs = self.get_certs_from_name(cert_name)
+            certs = self._get_certs_from_name(cert_name)
 
             for c in certs:
                 c.heuristics.verified_cpe_matches = {x.uri for x in cpes if x is not None} if cpes else None
 
-    def get_certs_from_name(self, name: str) -> List[CertSubType]:
+    def _get_certs_from_name(self, name: str) -> List[CertSubType]:
         raise NotImplementedError("Not meant to be implemented by the base class.")
 
     def enrich_automated_cpes_with_manual_labels(self) -> None:
diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py
index 2defd9f1..d70cf57f 100644
--- a/sec_certs/dataset/fips.py
+++ b/sec_certs/dataset/fips.py
@@ -1,14 +1,11 @@
 import logging
-import os
 import tempfile
-from itertools import groupby
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Set, Tuple
+from typing import Any, Dict, List, Optional, Set
 
 from bs4 import BeautifulSoup, NavigableString
 from graphviz import Digraph
 
-from sec_certs import constants as constants
 from sec_certs import helpers as helpers
 from sec_certs import parallel_processing as cert_processing
 from sec_certs.config.configuration import config
@@ -23,6 +20,10 @@ logger = logging.getLogger(__name__)
 
 
 class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
+    """
+    Class for processing of FIPSCertificate samples. Inherits from `ComplexSerializableType` and base abstract `Dataset` class.
+    """
+
     def __init__(
         self,
         certs: Dict[str, FIPSCertificate] = dict(),
@@ -36,48 +37,37 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
         self.new_files = 0
 
     @property
-    def results_dir(self) -> Path:
-        return self.root_dir / "results"
-
-    @property
-    def policies_dir(self) -> Path:
+    def _policies_dir(self) -> Path:
         return self.root_dir / "security_policies"
 
     @property
-    def fragments_dir(self) -> Path:
+    def _fragments_dir(self) -> Path:
         return self.root_dir / "fragments"
 
     @property
-    def algs_dir(self) -> Path:
+    def _algs_dir(self) -> Path:
         return self.web_dir / "algorithms"
 
-    # After web scan, there should be a FIPSCertificate object created for every entry
-    @property
-    def successful_web_scan(self) -> bool:
-        return all(self.certs) and all(cert.web_scan for cert in self.certs.values() if cert is not None)
-
-    @property
-    def successful_pdf_scan(self) -> bool:
-        return all(cert.pdf_scan for cert in self.certs.values() if cert is not None)
+    def _get_certs_from_name(self, module_name: str) -> List[FIPSCertificate]:
+        """
+        Returns list of certificates that match given name.
 
-    def get_certs_from_name(self, module_name: str) -> List[FIPSCertificate]:
+        :param str module_name: name to search for
+        :return List[FIPSCertificate]: List of certificates with web_scan.module_name == module_name
+        """
         return [crt for crt in self if crt.web_scan.module_name == module_name]
 
-    def find_empty_pdfs(self) -> Tuple[List, List]:
-        missing = []
-        not_available = []
-        for i in self.certs:
-            if not (self.policies_dir / f"{i}.pdf").exists():
-                missing.append(i)
-            elif os.path.getsize(self.policies_dir / f"{i}.pdf") < constants.FIPS_NOT_AVAILABLE_CERT_SIZE:
-                not_available.append(i)
-        return missing, not_available
-
     @serialize
     def pdf_scan(self, redo: bool = False) -> None:
+        """
+        pdf_scan()
+        Extracts data from pdf files
+
+        :param bool redo: Whether to try again with failed files, defaults to False
+        """
         logger.info("Entering PDF scan.")
 
-        self.fragments_dir.mkdir(parents=True, exist_ok=True)
+        self._fragments_dir.mkdir(parents=True, exist_ok=True)
 
         keywords = cert_processing.process_parallel(
             FIPSCertificate.find_keywords,
@@ -89,7 +79,7 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
         for keyword, cert in keywords:
             self.certs[cert.dgst].pdf_scan.keywords = keyword
 
-    def match_algs(self) -> Dict[str, int]:
+    def _match_algs(self) -> Dict[str, int]:
         output = {}
         for cert in self.certs.values():
             # if the pdf has not been processed, no matching can be done
@@ -103,18 +93,24 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
         return output
 
     def download_all_pdfs(self, cert_ids: Optional[Set[str]] = None) -> None:
+        """
+        Downloads all pdf files related to the certificates specified with cert_ids.
+
+        :param Optional[Set[str]] cert_ids: cert_ids to download the pdfs foor, defaults to None
+        :raises RuntimeError: If no cert_ids are specified, raises.
+        """
         sp_paths, sp_urls = [], []
-        self.policies_dir.mkdir(exist_ok=True)
+        self._policies_dir.mkdir(exist_ok=True)
         if cert_ids is None:
             raise RuntimeError("You need to provide cert ids to FIPS download PDFs functionality.")
         for cert_id in cert_ids:
-            if not (self.policies_dir / f"{cert_id}.pdf").exists() or (
+            if not (self._policies_dir / f"{cert_id}.pdf").exists() or (
                 fips_dgst(cert_id) in self.certs and not self.certs[fips_dgst(cert_id)].state.txt_state
             ):
                 sp_urls.append(
                     f"https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/security-policies/140sp{cert_id}.pdf"
                 )
-                sp_paths.append(self.policies_dir / f"{cert_id}.pdf")
+                sp_paths.append(self._policies_dir / f"{cert_id}.pdf")
         logger.info(f"downloading {len(sp_urls)} module pdf files")
         cert_processing.process_parallel(
             FIPSCertificate.download_security_policy,
@@ -124,7 +120,7 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
         )
         self.new_files += len(sp_urls)
 
-    def download_all_htmls(self, cert_ids: Set[str]) -> List[str]:
+    def _download_all_htmls(self, cert_ids: Set[str]) -> List[str]:
         html_paths, html_urls = [], []
         new_files = []
         self.web_dir.mkdir(exist_ok=True)
@@ -158,17 +154,20 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
 
     @serialize
     def convert_all_pdfs(self) -> None:
+        """
+        Converts all pdfs to text files
+        """
         logger.info("Converting FIPS sample reports to .txt")
         tuples = [
-            (cert, self.policies_dir / f"{cert.cert_id}.pdf", self.policies_dir / f"{cert.cert_id}.pdf.txt")
+            (cert, self._policies_dir / f"{cert.cert_id}.pdf", self._policies_dir / f"{cert.cert_id}.pdf.txt")
             for cert in self.certs.values()
-            if not cert.state.txt_state and (self.policies_dir / f"{cert.cert_id}.pdf").exists()
+            if not cert.state.txt_state and (self._policies_dir / f"{cert.cert_id}.pdf").exists()
         ]
         cert_processing.process_parallel(
             FIPSCertificate.convert_pdf_file, tuples, config.n_threads, progress_bar_desc="Converting to txt"
         )
 
-    def prepare_dataset(self, test: Optional[Path] = None, update: bool = False) -> Set[str]:
+    def _prepare_dataset(self, test: Optional[Path] = None, update: bool = False) -> Set[str]:
         if test:
             html_files = [test]
         else:
@@ -197,8 +196,8 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
 
         return cert_ids
 
-    def download_neccessary_files(self, cert_ids: Set[str]) -> None:
-        self.download_all_htmls(cert_ids)
+    def _download_neccessary_files(self, cert_ids: Set[str]) -> None:
+        self._download_all_htmls(cert_ids)
         self.download_all_pdfs(cert_ids)
 
     def _get_certificates_from_html(self, html_file: Path, update: bool = False) -> Set[str]:
@@ -220,15 +219,21 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
 
     @serialize
     def web_scan(self, cert_ids: Set[int], redo: bool = False) -> None:
+        """
+        Creates FIPSCertificate object from the relevant html file that must be downlaoded.
+
+        :param Set[int] cert_ids: Cert ids to create FIPSCertificate objects for.
+        :param bool redo: whether to re-attempt with failed certificates, defaults to False
+        """
         logger.info("Entering web scan.")
         for cert_id in cert_ids:
             dgst = fips_dgst(cert_id)
-            self.certs[dgst] = FIPSCertificate.html_from_file(
+            self.certs[dgst] = FIPSCertificate.from_html_file(
                 self.web_dir / f"{cert_id}.html",
                 FIPSCertificate.State(
-                    (self.policies_dir / str(cert_id)).with_suffix(".pdf"),
+                    (self._policies_dir / str(cert_id)).with_suffix(".pdf"),
                     (self.web_dir / str(cert_id)).with_suffix(".html"),
-                    (self.fragments_dir / str(cert_id)).with_suffix(".txt"),
+                    (self._fragments_dir / str(cert_id)).with_suffix(".txt"),
                     False,
                     None,
                     False,
@@ -239,6 +244,9 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
 
     @classmethod
     def from_web_latest(cls) -> "FIPSDataset":
+        """
+        Fetches the fresh snapshot of FIPSDataset from mirror.
+        """
         with tempfile.TemporaryDirectory() as tmp_dir:
             dset_path = Path(tmp_dir) / "fips_latest_dataset.json"
             logger.info("Downloading the latest FIPS dataset.")
@@ -254,14 +262,15 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
                 len(dset),
                 len(dset.algorithms) if dset.algorithms is not None else 0,
             )
-            logger.info("The dataset does not contain the results of the dependency analysis - calculating them now...")
-            dset.finalize_results()
+            # TODO: Fixme, this is really costly
+            # logger.info("The dataset does not contain the results of the dependency analysis - calculating them now...")
+            # dset.finalize_results()
             return dset
 
-    def set_local_paths(self) -> None:
+    def _set_local_paths(self) -> None:
         cert: FIPSCertificate
         for cert in self.certs.values():
-            cert.set_local_paths(self.policies_dir, self.web_dir, self.fragments_dir)
+            cert.set_local_paths(self._policies_dir, self.web_dir, self._fragments_dir)
 
     @serialize
     def get_certs_from_web(
@@ -284,11 +293,11 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
         logger.info("Downloading required html files")
 
         self.web_dir.mkdir(parents=True, exist_ok=True)
-        self.policies_dir.mkdir(exist_ok=True)
-        self.algs_dir.mkdir(exist_ok=True)
+        self._policies_dir.mkdir(exist_ok=True)
+        self._algs_dir.mkdir(exist_ok=True)
 
         # Download files containing all available module certs (always)
-        cert_ids = self.prepare_dataset(test, update)
+        cert_ids = self._prepare_dataset(test, update)
 
         if not no_download_algorithms:
             aset = FIPSAlgorithmDataset({}, Path(self.root_dir / "web" / "algorithms"), "algorithms", "sample algs")
@@ -298,23 +307,11 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
             self.algorithms = aset
 
         logger.info("Downloading certificate html and security policies")
-        self.download_neccessary_files(cert_ids)
+        self._download_neccessary_files(cert_ids)
 
         self.web_scan(cert_ids, redo=redo_web_scan)
 
     @serialize
-    def deprocess(self) -> None:
-        # TODO think of a better way to make resulting json smaller and easier to share
-        logger.info(
-            "Removing 'heuristics' field. This dataset can be used to be uploaded and later downloaded using latest_snapshot() or something"
-        )
-        cert: FIPSCertificate
-        for cert in self.certs.values():
-            cert.heuristics = FIPSCertificate.FIPSHeuristics(dict(), [], 0)
-
-        self.match_algs()
-
-    @serialize
     def extract_certs_from_tables(self, high_precision: bool) -> List[Path]:
         """
         Function that extracts algorithm IDs from tables in security policies files.
@@ -341,11 +338,11 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
             certificate.pdf_scan.algorithms += algorithms
         return not_decoded
 
-    def remove_algorithms_from_extracted_data(self) -> None:
+    def _remove_algorithms_from_extracted_data(self) -> None:
         for cert in self.certs.values():
             cert.remove_algorithms()
 
-    def unify_algorithms(self) -> None:
+    def _unify_algorithms(self) -> None:
         for certificate in self.certs.values():
             new_algorithms: List[Dict] = []
             united_algorithms = [
@@ -439,7 +436,7 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
                 return False
         return True
 
-    def validate_results(self) -> None:
+    def _validate_results(self) -> None:
         """
         Function that validates results and finds the final connection output
         """
@@ -481,10 +478,16 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
 
     @serialize
     def finalize_results(self, use_nist_cpe_matching_dict: bool = True, perform_cpe_heuristics: bool = True):
+        """
+        Performs processing of extracted data. Computes all heuristics.
+
+        :param bool use_nist_cpe_matching_dict: If NIST CPE matching dictionary shall be used to drive computing related CVEs, defaults to True
+        :param bool perform_cpe_heuristics: If CPE heuristics shall be computed, defaults to True
+        """
         logger.info("Entering 'analysis' and building connections between certificates.")
-        self.unify_algorithms()
-        self.remove_algorithms_from_extracted_data()
-        self.validate_results()
+        self._unify_algorithms()
+        self._remove_algorithms_from_extracted_data()
+        self._validate_results()
         if perform_cpe_heuristics:
             _, _, cve_dset = self.compute_cpe_heuristics()
             self.compute_related_cves(use_nist_cpe_matching_dict=use_nist_cpe_matching_dict, cve_dset=cve_dset)
@@ -520,7 +523,7 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
         attr = {"pdf": "pdf_scan", "web": "web_scan", "heuristics": "heuristics"}[connection_list]
         return getattr(self.certs[dgst], attr).connections
 
-    def create_dot_graph(
+    def _create_dot_graph(
         self,
         output_file_name: str,
         connection_list: str = "heuristics",
@@ -585,6 +588,11 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
         single_dot.render(self.root_dir / (str(output_file_name) + "_single"), view=show)
 
     def to_dict(self) -> Dict[str, Any]:
+        """
+        Serializes dataset into a dictionary
+
+        :return Dict[str, Any]: Dictionary that holds the whole dataset.
+        """
         return {
             "timestamp": self.timestamp,
             "sha256_digest": self.sha256_digest,
@@ -597,6 +605,12 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
 
     @classmethod
     def from_dict(cls, dct: Dict[str, Any]) -> "FIPSDataset":
+        """
+        Reconstructs the original dataset from a dictionary
+
+        :param Dict[str, Any] dct: Dictionary that holds the serialized dataset
+        :return FIPSDataset: Deserialized FIPSDataset that corresponds to `dct` contents
+        """
         certs = dct["certs"]
         dset = cls(certs, Path("./"), dct["name"], dct["description"])
         dset.algorithms = dct["algs"]
@@ -606,16 +620,12 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType):
             )
         return dset
 
-    def group_vendors(self) -> Dict[str, List[str]]:
-        vendors: Dict[str, List[str]] = {}
-        v = {x.web_scan.vendor.lower() for x in self.certs.values() if x is not None and x.web_scan.vendor is not None}
-        v_sorted = sorted(v, key=FIPSCertificate.get_compare)
-        for prefix, a in groupby(v_sorted, key=FIPSCertificate.get_compare):
-            vendors[prefix] = list(a)
-
-        return vendors
-
     def plot_graphs(self, show: bool = False) -> None:
-        self.create_dot_graph("full_graph", show=show)
-        self.create_dot_graph("web_only_graph", "web", show=show)
-        self.create_dot_graph("pdf_only_graph", "pdf", show=show)
+        """
+        Plots FIPS graphs.
+        # TODO: Currently broken, see https://github.com/crocs-muni/sec-certs/issues/211
+        :param bool show: If plots should be showed with .show() method, defaults to False
+        """
+        self._create_dot_graph("full_graph", show=show)
+        self._create_dot_graph("web_only_graph", "web", show=show)
+        self._create_dot_graph("pdf_only_graph", "pdf", show=show)
diff --git a/sec_certs/model/cpe_matching.py b/sec_certs/model/cpe_matching.py
index 1dcf8801..7b25b2a2 100644
--- a/sec_certs/model/cpe_matching.py
+++ b/sec_certs/model/cpe_matching.py
@@ -34,30 +34,34 @@ class CPEClassifier(BaseEstimator):
     def fit(self, X: List[CPE], y: Optional[List[str]] = None) -> "CPEClassifier":
         """
         Just creates look-up structures from provided list of CPEs
-        @param X: List of CPEs that can be matched with predict()
-        @param y: will be ignored, specified to adhere to sklearn BaseEstimator interface
+
+        :param List[CPE] X: List of CPEs that can be matched with predict()
+        :param Optional[List[str]] y: will be ignored, specified to adhere to sklearn BaseEstimator interface, defaults to None
+        :return CPEClassifier: return self to allow method chaining
         """
-        self.build_lookup_structures(X)
+        self._build_lookup_structures(X)
         return self
 
     @staticmethod
-    def filter_short_cpes(cpes: List[CPE]) -> List[CPE]:
+    def _filter_short_cpes(cpes: List[CPE]) -> List[CPE]:
         """
         Short CPE items are super easy to match with 100% rank, but they are hardly informative. This method discards them.
-        @param cpes: List of CPEs to filtered
-        @return All CPEs in cpes variable which item name has at least 4 characters.
+
+        :param List[CPE] cpes: List of CPEs to filtered
+        :return List[CPE]: All CPEs in cpes variable which item name has at least 4 characters.
         """
         return list(filter(lambda x: x.item_name is not None and len(x.item_name) > 3, cpes))
 
-    def build_lookup_structures(self, X: List[CPE]) -> None:
+    def _build_lookup_structures(self, X: List[CPE]) -> None:
         """
         Builds several look-up dictionaries for fast matching.
         - vendor_to_version_: each vendor is mapped to set of versions that appear in combination with vendor in CPE dataset
         - vendor_version_to_cpe_: Each (vendor, version) tuple is mapped to a set of CPE items that appear in combination with this tuple in CPE dataset
         - vendors_: Just aggregates set of vendors, used for prunning later on.
-        @param X: List of CPEs that will be used to build the dictionaries
+
+        :param List[CPE] X: List of CPEs that will be used to build the dictionaries
         """
-        sufficiently_long_cpes = self.filter_short_cpes(X)
+        sufficiently_long_cpes = self._filter_short_cpes(X)
         self.vendor_to_versions_ = {x.vendor: set() for x in sufficiently_long_cpes}
         self.vendors_ = set(self.vendor_to_versions_.keys())
         self.vendor_version_to_cpe_ = dict()
@@ -72,8 +76,9 @@ class CPEClassifier(BaseEstimator):
     def predict(self, X: List[Tuple[str, str, str]]) -> List[Optional[Set[str]]]:
         """
         Will predict CPE uris for List of Tuples (vendor, product name, identified versions in product name)
-        @param X: tuples (vendor, product name, identified versions in product name)
-        @return: List of CPE uris that correspond to given input, None if nothing was found.
+
+        :param List[Tuple[str, str, str]] X: tuples (vendor, product name, identified versions in product name)
+        :return List[Optional[Set[str]]]: List of CPE uris that correspond to given input, None if nothing was found.
         """
         return [self.predict_single_cert(x[0], x[1], x[2]) for x in helpers.tqdm(X, desc="Predicting")]
 
@@ -93,23 +98,23 @@ class CPEClassifier(BaseEstimator):
         4. Compute string similarity of the candidate CPE matches and certificate name
         5. Evaluate best string similarity, if above threshold, declare it a match.
         6. If no CPE item is matched, try again but relax version and check CPEs that don't have their version specified.
-        Also, search for 100% CPE matches on item name instead of title.
-        @param vendor: manufacturer of the certificate
-        @param product_name: name of the certificate
-        @param versions: List of versions that appear in the certificate name
-        @param relax_version: bool, see step 6 above.
-        @param relax_title: bool
-        @return:
-        """
+        7. (Also, search for 100% CPE matches on item name instead of title.)
 
+        :param Optional[str] vendor: manufacturer of the certificate
+        :param str product_name: name of the certificate
+        :param Set[str] versions: List of versions that appear in the certificate name
+        :param bool relax_version: See step 6 above., defaults to False
+        :param bool relax_title: See step 7 above, defaults to False
+        :return Optional[Set[str]]: Set of matching CPE uris, None if no matches found
+        """
         lemmatized_product_name = self._lemmatize_product_name(product_name)
-        candidate_vendors = self.get_candidate_list_of_vendors(
+        candidate_vendors = self._get_candidate_list_of_vendors(
             CPEClassifier._discard_trademark_symbols(vendor).lower() if vendor else vendor
         )
-        candidates = self.get_candidate_cpe_matches(candidate_vendors, versions)
+        candidates = self._get_candidate_cpe_matches(candidate_vendors, versions)
 
         ratings = [
-            self.compute_best_match(cpe, lemmatized_product_name, candidate_vendors, versions, relax_title=relax_title)
+            self._compute_best_match(cpe, lemmatized_product_name, candidate_vendors, versions, relax_title=relax_title)
             for cpe in candidates
         ]
         threshold = self.match_threshold if not relax_version else 100
@@ -131,7 +136,7 @@ class CPEClassifier(BaseEstimator):
 
         return final_matches if final_matches else None
 
-    def compute_best_match(
+    def _compute_best_match(
         self,
         cpe: CPE,
         product_name: str,
@@ -143,11 +148,13 @@ class CPEClassifier(BaseEstimator):
         Tries several different settings in which string similarity between CPE and certificate name is tested.
         For definition of string similarity, see rapidfuzz package on GitHub. Both token set ratio and partial ratio are tested,
         always both on CPE title and CPE item name.
-        @param cpe: CPE to test
-        @param product_name: name of the certificate
-        @param candidate_vendors: vendors that appear in the certificate
-        @param versions: versions that appear in the certificate
-        @return: Maximal value of the four string similarities discussed above.
+
+        :param CPE cpe: CPE to test
+        :param str product_name: name of the certificate
+        :param Optional[Set[str]] candidate_vendors: vendors that appear in the certificate
+        :param Set[str] versions: versions that appear in the certificate
+        :param bool relax_title: if to relax title or not, defaults to False
+        :return float: Maximal value of the four string similarities discussed above.
         """
         if relax_title:
             sanitized_title = (
@@ -225,16 +232,17 @@ class CPEClassifier(BaseEstimator):
         if "athena" in tokenized and "smartcard" in tokenized:
             result.add("athena-scs")
         if tokenized[0] == "the" and not result:
-            candidate_result = self.get_candidate_list_of_vendors(" ".join(tokenized[1:]))
+            candidate_result = self._get_candidate_list_of_vendors(" ".join(tokenized[1:]))
             return set(candidate_result) if candidate_result else set()
 
         return set(result) if result else set()
 
-    def get_candidate_list_of_vendors(self, manufacturer: Optional[str]) -> Set[str]:
+    def _get_candidate_list_of_vendors(self, manufacturer: Optional[str]) -> Set[str]:
         """
         Given manufacturer name, this method will find list of plausible vendors from CPE dataset that are likely related.
-        @param manufacturer: manufacturer
-        @return: List of related manufacturers, None if nothing relevant is found.
+
+        :param Optional[str] manufacturer: manufacturer
+        :return Set[str]: List of related manufacturers, None if nothing relevant is found.
         """
         result: Set[str] = set()
         if not manufacturer:
@@ -246,7 +254,7 @@ class CPEClassifier(BaseEstimator):
             vendor_tokens = set(
                 itertools.chain.from_iterable([[x.strip() for x in manufacturer.split(s)] for s in splits])
             )
-            result_aux = [self.get_candidate_list_of_vendors(x) for x in vendor_tokens]
+            result_aux = [self._get_candidate_list_of_vendors(x) for x in vendor_tokens]
             result_used = set(set(itertools.chain.from_iterable([x for x in result_aux if x])))
             return result_used if result_used else set()
 
@@ -255,14 +263,16 @@ class CPEClassifier(BaseEstimator):
 
         return self._process_manufacturer(manufacturer, result)
 
-    def get_candidate_vendor_version_pairs(
+    def _get_candidate_vendor_version_pairs(
         self, cert_candidate_cpe_vendors: Set[str], cert_candidate_versions: Set[str]
     ) -> Optional[List[Tuple[str, str]]]:
         """
         Given parameters, will return Pairs (cpe_vendor, cpe_version) that are relevant to a given sample
-        @param cert_candidate_cpe_vendors: list of CPE vendors relevant to a sample
-        @param cert_candidate_versions: List of versions heuristically extracted from the sample name
-        @return: List of tuples (cpe_vendor, cpe_version) that can be used in the lookup table to search the CPE dataset.
+
+
+        :param Set[str] cert_candidate_cpe_vendors: list of CPE vendors relevant to a sample
+        :param Set[str] cert_candidate_versions: List of versions heuristically extracted from the sample name
+        :return Optional[List[Tuple[str, str]]]: List of tuples (cpe_vendor, cpe_version) that can be used in the lookup table to search the CPE dataset.
         """
 
         def is_cpe_version_among_cert_versions(cpe_version: Optional[str], cert_versions: Set[str]) -> bool:
@@ -296,14 +306,15 @@ class CPEClassifier(BaseEstimator):
             candidate_vendor_version_pairs.extend([(vendor, x) for x in matched_cpe_versions])
         return candidate_vendor_version_pairs
 
-    def get_candidate_cpe_matches(self, candidate_vendors: Set[str], candidate_versions: Set[str]) -> List[CPE]:
+    def _get_candidate_cpe_matches(self, candidate_vendors: Set[str], candidate_versions: Set[str]) -> List[CPE]:
         """
         Given List of candidate vendors and candidate versions found in certificate, candidate CPE matches are found
-        @param candidate_vendors: List of version strings that were found in the certificate
-        @param candidate_versions: List of vendor strings that were found in the certificate
-        @return:
+
+        :param Set[str] candidate_vendors: List of version strings that were found in the certificate
+        :param Set[str] candidate_versions: List of vendor strings that were found in the certificate
+        :return List[CPE]: List of CPE records that could match, to be refined later
         """
-        candidate_vendor_version_pairs = self.get_candidate_vendor_version_pairs(candidate_vendors, candidate_versions)
+        candidate_vendor_version_pairs = self._get_candidate_vendor_version_pairs(candidate_vendors, candidate_versions)
         return (
             list(
                 itertools.chain.from_iterable([self.vendor_version_to_cpe_[x] for x in candidate_vendor_version_pairs])
diff --git a/sec_certs/model/dependency_finder.py b/sec_certs/model/dependency_finder.py
index afc4db59..8410a9c5 100644
--- a/sec_certs/model/dependency_finder.py
+++ b/sec_certs/model/dependency_finder.py
@@ -11,6 +11,12 @@ ReferenceLookupFunc = Callable[[Certificate], Set[str]]
 
 
 class DependencyFinder:
+    """
+    The class assigns references of other certificate instances for each instance.
+    Adheres to sklearn BaseEstimator interface.
+    The fit is called on a dictionary of certificates, builds a hashmap of references, and assigns references for each certificate in the dictionary.
+    """
+
     def __init__(self):
         self.dependencies: Dependencies = {}
 
@@ -96,6 +102,14 @@ class DependencyFinder:
         return referenced_by_indirect.get(cert, None)
 
     def fit(self, certificates: Certificates, id_func: IDLookupFunc, ref_lookup_func: ReferenceLookupFunc) -> None:
+        """
+        Builds a list of references and assigns references for each certificate instance.
+
+        :param Certificates certificates: dictionary of certificates with hashes as key
+        :param IDLookupFunc id_func: lookup function for cert id
+        :param ReferenceLookupFunc ref_lookup_func: lookup for references
+        :return None: None
+        """
         referenced_by_direct, referenced_by_indirect = DependencyFinder._build_cert_references(
             certificates, id_func, ref_lookup_func
         )
@@ -140,6 +154,12 @@ class DependencyFinder:
         return set(res) if res else None
 
     def predict_single_cert(self, dgst: str) -> References:
+        """
+        Method returns references object for specified certificate digest
+
+        :param str dgst: certificate digest
+        :return References: References object
+        """
         return References(
             self._get_directly_referenced_by(dgst),
             self._get_indirectly_referenced_by(dgst),
@@ -148,6 +168,12 @@ class DependencyFinder:
         )
 
     def predict(self, dgst_list: List[str]) -> Dict[str, References]:
+        """
+        Method returns references for a list of certificate digests
+
+        :param List[str] dgst_list: List of certificate hashes
+        :return Dict[str, References]: Dict with certificate hash and References object.
+        """
         cert_references = {}
 
         for dgst in dgst_list:
diff --git a/sec_certs/model/dependency_vulnerability_finder.py b/sec_certs/model/dependency_vulnerability_finder.py
index 2f66f30c..dd25774d 100644
--- a/sec_certs/model/dependency_vulnerability_finder.py
+++ b/sec_certs/model/dependency_vulnerability_finder.py
@@ -23,6 +23,11 @@ Vulnerabilities = Dict[str, Dict[str, Optional[Set[str]]]]
 
 
 class DependencyVulnerabilityFinder:
+    """
+    The class assigns vulnerabilities to each certificate instance caused by dependencies among certificate instances.
+    Adheres to sklearn BaseEstimator interface.
+    """
+
     def __init__(self):
         self.vulnerabilities: Vulnerabilities = {}
         self.certificates: Certificates = {}
@@ -77,6 +82,12 @@ class DependencyVulnerabilityFinder:
         return vulnerabilities if vulnerabilities else None
 
     def fit(self, certificates: Certificates) -> Vulnerabilities:
+        """
+        Method assigns each certificate vulnerabilities caused by dependencies among certificates
+
+        :param Certificates certificates: Dictionary of certificates with digests
+        :return Vulnerabilities: Dictionary of vulnerabilities of certificate instances
+        """
         self._overwrite_previous_state(certificates)
 
         cert_id_occurrences = self._get_dataset_cert_ids_occurrences()
@@ -106,6 +117,12 @@ class DependencyVulnerabilityFinder:
         return self.vulnerabilities
 
     def predict_single_cert(self, dgst: str) -> DependencyCVE:
+        """
+        Method returns vulnerabilities for certificate digest
+
+        :param str dgst: Digest of certificate
+        :return DependencyCVE: DependencyCVE object of certificate
+        """
         if not self.vulnerabilities.get(dgst):
             return DependencyCVE(direct_dependency_cves=None, indirect_dependency_cves=None)
 
@@ -115,6 +132,12 @@ class DependencyVulnerabilityFinder:
         )
 
     def predict(self, dgst_list: List[str]) -> Dict[str, DependencyCVE]:
+        """
+        Method returns vulnerabilities for a list of certificate digests
+
+        :param List[str] dgst_list: list of certificate digests
+        :return Dict[str, DependencyCVE]: Dictionary of DependencyCVE objects for specified certificate digests
+        """
         cert_vulnerabilities = {}
 
         for dgst in dgst_list:
diff --git a/sec_certs/model/sar_transformer.py b/sec_certs/model/sar_transformer.py
index ee99b279..58f96c0b 100644
--- a/sec_certs/model/sar_transformer.py
+++ b/sec_certs/model/sar_transformer.py
@@ -19,17 +19,38 @@ class SARTransformer(BaseEstimator, TransformerMixin):
     """
 
     def fit(self, certificates: Iterable[CommonCriteriaCert]) -> SARTransformer:
+        """
+        Just returns self, no fitting needed
+
+        :param Iterable[CommonCriteriaCert] certificates: Unused parameter
+        :return SARTransformer: return self
+        """
         return self
 
     def transform(self, certificates: Iterable[CommonCriteriaCert]) -> List[Optional[Set[SAR]]]:
+        """
+        Just a wrapper around transform_single_cert() called on an iterable of CommonCriteriaCert.
+
+        :param Iterable[CommonCriteriaCert] certificates: Iterable of CommonCriteriaCert objects to perform the extraction on.
+        :return List[Optional[Set[SAR]]]: Returns List of results from transform_single_cert().
+        """
         return [self.transform_single_cert(cert) for cert in certificates]
 
     def transform_single_cert(self, cert: CommonCriteriaCert) -> Optional[Set[SAR]]:
-        sec_level_candidates, st_candidates, report_candidates = self.collect_sar_candidates_from_all_sources(cert)
-        return self.resolve_candidate_conflicts(sec_level_candidates, st_candidates, report_candidates, cert.dgst)
+        """
+        Given CommonCriteriaCert, will transform SAR keywords extracted from txt files
+        into a set of SAR objects. Also handles extractin of correct SAR levels, duplicities and filtering.
+        Uses three sources: CSV scan, security target, and certification report.
+        The caller should assure that the certificates have the keywords extracted.
+
+        :param CommonCriteriaCert cert: Certificate to extract SARs from
+        :return Optional[Set[SAR]]: Set of SARs, None if none were identified.
+        """
+        sec_level_candidates, st_candidates, report_candidates = self._collect_sar_candidates_from_all_sources(cert)
+        return self._resolve_candidate_conflicts(sec_level_candidates, st_candidates, report_candidates, cert.dgst)
 
     @staticmethod
-    def collect_sar_candidates_from_all_sources(cert: CommonCriteriaCert) -> Tuple[Set[SAR], Set[SAR], Set[SAR]]:
+    def _collect_sar_candidates_from_all_sources(cert: CommonCriteriaCert) -> Tuple[Set[SAR], Set[SAR], Set[SAR]]:
         """
         Parses SARs from three distinct sources and returns the results as a three tuple:
         - Security level from CSV scan
@@ -43,24 +64,24 @@ class SARTransformer(BaseEstimator, TransformerMixin):
         def report_keywords_may_have_sars(sample: CommonCriteriaCert):
             return sample.pdf_data.report_keywords and SAR_DICT_KEY in sample.pdf_data.report_keywords
 
-        sec_level_sars = SARTransformer.parse_sars_from_security_level_list(cert.security_level)
+        sec_level_sars = SARTransformer._parse_sars_from_security_level_list(cert.security_level)
 
         if st_keywords_may_have_sars(cert):
             st_dict: Dict = cast(Dict, cert.pdf_data.st_keywords)
-            st_sars = SARTransformer.parse_sar_dict(st_dict[SAR_DICT_KEY], cert.dgst)
+            st_sars = SARTransformer._parse_sar_dict(st_dict[SAR_DICT_KEY], cert.dgst)
         else:
             st_sars = set()
 
         if report_keywords_may_have_sars(cert):
             report_dict: Dict = cast(Dict, cert.pdf_data.report_keywords)
-            report_sars = SARTransformer.parse_sar_dict(report_dict[SAR_DICT_KEY], cert.dgst)
+            report_sars = SARTransformer._parse_sar_dict(report_dict[SAR_DICT_KEY], cert.dgst)
         else:
             report_sars = set()
 
         return sec_level_sars, st_sars, report_sars
 
     @staticmethod
-    def resolve_candidate_conflicts(
+    def _resolve_candidate_conflicts(
         sec_level_candidates: Set[SAR], st_candidates: Set[SAR], report_candidates: Set[SAR], cert_dgst: str
     ) -> Optional[Set[SAR]]:
         final_candidates: Dict[str, SAR] = {x.family: x for x in sec_level_candidates}
@@ -94,7 +115,7 @@ class SARTransformer(BaseEstimator, TransformerMixin):
         return set(final_candidates.values()) if final_candidates else None
 
     @staticmethod
-    def parse_sar_dict(dct: Dict[str, int], dgst: str) -> Set[SAR]:
+    def _parse_sar_dict(dct: Dict[str, int], dgst: str) -> Set[SAR]:
         """
         Accepts st_keywords or report_keywords dictionary. Will reconstruct SAR objects from it. Each SAR family can
         appear multiple times in the dictionary (due to conflicts) with different levels. Iterated item will replace
@@ -126,7 +147,7 @@ class SARTransformer(BaseEstimator, TransformerMixin):
         return {x[0] for x in sars.values()} if sars else set()
 
     @staticmethod
-    def parse_sars_from_security_level_list(lst: Iterable[str]) -> Set[SAR]:
+    def _parse_sars_from_security_level_list(lst: Iterable[str]) -> Set[SAR]:
         sars = set()
         for element in lst:
             try:
diff --git a/sec_certs/pandas_helpers.py b/sec_certs/pandas_helpers.py
index 2847a167..dca1feb4 100644
--- a/sec_certs/pandas_helpers.py
+++ b/sec_certs/pandas_helpers.py
@@ -38,38 +38,42 @@ def compute_cve_correlations(
     filter_nans: bool = True,
 ) -> pd.DataFrame:
     """
-    Computes correlations of EAL and different SARs and two columns: (n_cves, worst_cve). Few assumptions about the passed dataframe:
+    Computes correlations of EAL and different SARs and two columns: (n_cves, worst_cve_score, avg_cve_score). Few assumptions about the passed dataframe:
     - EAL column must be categorical data type
     - SAR column must be a set of SARs
-    - `n_cves` and `worst_cve` columns must be present in the dataframe
+    - `n_cves` and `worst_cve_score`, `avg_cve_score` columns must be present in the dataframe
     Possibly, it can filter columns will both values NaN (due to division by zero or super low supports.)
     """
-    df_sar = df.loc[:, ["highest_security_level", "sars", "worst_cve", "n_cves"]]
-    df_sar = df_sar.rename(columns={"highest_security_level": "EAL"})
-    families = discover_sar_families(df_sar.sars)
-    df_sar.EAL = df_sar.EAL.cat.codes
+    df_sar = df.loc[:, ["eal", "extracted_sars", "worst_cve_score", "avg_cve_score", "n_cves"]]
+    families = discover_sar_families(df_sar.extracted_sars)
+    df_sar.eal = df_sar.eal.cat.codes
 
     if exclude_vuln_free_certs:
         df_sar = df_sar.loc[df_sar.n_cves > 0]
 
-    n_cves_corrs = [df_sar["EAL"].corr(df_sar.n_cves)]
-    worst_cve_corrs = [df_sar["EAL"].corr(df_sar.worst_cve)]
-    supports = [df_sar.loc[~df_sar["EAL"].isnull()].shape[0]]
+    n_cves_corrs = [df_sar["eal"].corr(df_sar.n_cves)]
+    worst_cve_corrs = [df_sar["eal"].corr(df_sar.worst_cve_score)]
+    avg_cve_corrs = [df_sar["eal"].corr(df_sar.avg_cve_score)]
+    supports = [df_sar.loc[~df_sar["eal"].isnull()].shape[0]]
 
     for family in tqdm(families):
-        df_sar[family] = df_sar.sars.map(lambda x: get_sar_level_from_set(x, family))
+        df_sar[family] = df_sar.extracted_sars.map(lambda x: get_sar_level_from_set(x, family))
         n_cves_corrs.append(df_sar[family].corr(df_sar.n_cves))
-        worst_cve_corrs.append(df_sar[family].corr(df_sar.worst_cve))
+        worst_cve_corrs.append(df_sar[family].corr(df_sar.worst_cve_score))
+        avg_cve_corrs.append(df_sar[family].corr(df_sar.avg_cve_score))
         supports.append(df_sar.loc[~df_sar[family].isnull()].shape[0])
     df_sar = df_sar.copy()
 
-    tuples = list(zip(n_cves_corrs, worst_cve_corrs, supports))
-    dct = {family: correlations for family, correlations in zip(["EAL"] + families, tuples)}
-    df_corr = pd.DataFrame.from_dict(dct, orient="index", columns=["n_cves_corr", "worst_cve_corr", "support"])
+    tuples = list(zip(n_cves_corrs, worst_cve_corrs, avg_cve_corrs, supports))
+    dct = {family: correlations for family, correlations in zip(["eal"] + families, tuples)}
+    df_corr = pd.DataFrame.from_dict(
+        dct, orient="index", columns=["n_cves_corr", "worst_cve_score_corr", "avg_cve_score_corr", "support"]
+    )
     df_corr.style.set_caption("Correlations between EAL, SARs and CVEs")
+    df_corr = df_corr.sort_values(by="support", ascending=False)
 
     if filter_nans:
-        df_corr = df_corr.dropna(how="all", subset=["n_cves_corr", "worst_cve_corr"])
+        df_corr = df_corr.dropna(how="all", subset=["n_cves_corr", "worst_cve_score_corr", "avg_cve_score_corr"])
 
     if output_path:
         df_corr.to_csv(output_path)
@@ -92,9 +96,19 @@ def expand_cc_df_with_cve_cols(cc_df: pd.DataFrame, cve_dset: CVEDataset) -> pd.
         lambda x: [cve_dset[y].published_date.date() for y in x] if x is not np.nan else np.nan  # type: ignore
     )
     df["earliest_cve"] = df.cve_published_dates.map(lambda x: min(x) if isinstance(x, list) else np.nan)
-    df["worst_cve"] = df.related_cves.map(
+    df["worst_cve_score"] = df.related_cves.map(
         lambda x: max([cve_dset[cve].impact.base_score for cve in x]) if x is not np.nan else np.nan
     )
+
+    """
+    Note: Technically, CVE can have 0 base score. This happens when the CVE is discarded from the database.
+    This could skew the results. During May 2022 analysis, we encountered a single CVE with such score.
+    Therefore, we do not treat this case.
+    To properly treat this, the average should be taken across CVEs with >0 base_socre.
+    """
+    df["avg_cve_score"] = df.related_cves.map(
+        lambda x: np.mean([cve_dset[cve].impact.base_score for cve in x]) if x is not np.nan else np.nan
+    )
     return df
 
 
diff --git a/sec_certs/sample/common_criteria.py b/sec_certs/sample/common_criteria.py
index 47ea3190..a9721672 100644
--- a/sec_certs/sample/common_criteria.py
+++ b/sec_certs/sample/common_criteria.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import copy
 import operator
 import re
@@ -39,6 +41,13 @@ class CommonCriteriaCert(
     PandasSerializableType,
     ComplexSerializableType,
 ):
+    """
+    Data structure for common criteria certificate. Contains several inner classes that layer the data logic.
+    Can be serialized into/from json (`ComplexSerializableType`) or pandas (`PandasSerializableType)`.
+    Is basic element of `CCDataset`. The functionality is mostly related to holding data and transformations that
+    the certificate can handle itself. `CCDataset` class then instrument this functionality.
+    """
+
     cc_url = "http://www.commoncriteriaportal.org"
     empty_st_url = "http://www.commoncriteriaportal.org/files/epfiles/"
 
@@ -60,7 +69,7 @@ class CommonCriteriaCert(
             super().__setattr__("maintenance_date", helpers.sanitize_date(self.maintenance_date))
 
         @classmethod
-        def from_dict(cls, dct: Dict) -> "CommonCriteriaCert.MaintenanceReport":
+        def from_dict(cls, dct: Dict) -> CommonCriteriaCert.MaintenanceReport:
             new_dct = dct.copy()
             new_dct["maintenance_date"] = (
                 date.fromisoformat(dct["maintenance_date"])
@@ -74,6 +83,11 @@ class CommonCriteriaCert(
 
     @dataclass(init=False)
     class InternalState(ComplexSerializableType):
+        """
+        Holds internal state of the dataset, whether downloads and converts of individual components succeeded. Also
+        holds information about errors and paths to the files.
+        """
+
         st_download_ok: bool
         report_download_ok: bool
         st_convert_ok: bool
@@ -143,6 +157,10 @@ class CommonCriteriaCert(
 
     @dataclass
     class PdfData(ComplexSerializableType):
+        """
+        Class that holds data extracted from pdf files.
+        """
+
         report_metadata: Optional[Dict[str, Any]] = field(default=None)
         st_metadata: Optional[Dict[str, Any]] = field(default=None)
         report_frontpage: Optional[Dict[str, Dict[str, Any]]] = field(default=None)
@@ -155,26 +173,44 @@ class CommonCriteriaCert(
 
         @property
         def bsi_data(self) -> Optional[Dict[str, Any]]:
+            """
+            Returns frontpage data related to BSI-provided information
+            """
             return self.report_frontpage.get("bsi", None) if self.report_frontpage else None
 
         @property
         def niap_data(self) -> Optional[Dict[str, Any]]:
+            """
+            Returns frontpage data related to niap-provided information
+            """
             return self.report_frontpage.get("niap", None) if self.report_frontpage else None
 
         @property
         def nscib_data(self) -> Optional[Dict[str, Any]]:
+            """
+            Returns frontpage data related to nscib-provided information
+            """
             return self.report_frontpage.get("nscib", None) if self.report_frontpage else None
 
         @property
         def canada_data(self) -> Optional[Dict[str, Any]]:
+            """
+            Returns frontpage data related to canada-provided information
+            """
             return self.report_frontpage.get("canada", None) if self.report_frontpage else None
 
         @property
         def anssi_data(self) -> Optional[Dict[str, Any]]:
+            """
+            Returns frontpage data related to ANSSI-provided information
+            """
             return self.report_frontpage.get("anssi", None) if self.report_frontpage else None
 
         @property
         def cert_lab(self) -> Optional[List[str]]:
+            """
+            Returns labs for which certificate data was parsed.
+            """
             labs = [
                 data["cert_lab"].split(" ")[0].upper()
                 for data in [self.bsi_data, self.anssi_data, self.niap_data, self.nscib_data, self.canada_data]
@@ -204,6 +240,9 @@ class CommonCriteriaCert(
 
         @property
         def processed_cert_id(self) -> Optional[str]:
+            """
+            Returns processed cert id extracted from the pdf data.
+            """
             cert_ids = set(
                 filter(
                     lambda x: x,
@@ -225,7 +264,8 @@ class CommonCriteriaCert(
         @property
         def keywords_cert_id(self) -> Optional[str]:
             """
-            :return: the most occurring among cert ids captured in keywords scan
+            Returns the most frequently appearing cert id. If you don't know why to use this, you should probably use
+            `cert_id` property.
             """
             if not self.keywords_rules_cert_id:
                 return None
@@ -236,10 +276,17 @@ class CommonCriteriaCert(
 
         @property
         def cert_id(self) -> Optional[str]:
-            return processed if (processed := self.processed_cert_id) else self.keywords_cert_id
+            """
+            Returns `processed_cert_id` if it exists, else return `keyword_cert_id`
+            """
+            return self.processed_cert_id if self.processed_cert_id else self.keywords_cert_id
 
     @dataclass
     class CCHeuristics(Heuristics, ComplexSerializableType):
+        """
+        Class for various heuristics related to CommonCriteriaCert
+        """
+
         extracted_versions: Optional[Set[str]] = field(default=None)
         cpe_matches: Optional[Set[str]] = field(default=None)
         verified_cpe_matches: Optional[Set[str]] = field(default=None)
@@ -324,7 +371,7 @@ class CommonCriteriaCert(
         self.maintenance_updates = maintenance_updates
         self.state = self.InternalState() if not state else state
         self.pdf_data = self.PdfData() if not pdf_data else pdf_data
-        self.heuristics: "CommonCriteriaCert.CCHeuristics" = self.CCHeuristics() if not heuristics else heuristics
+        self.heuristics: CommonCriteriaCert.CCHeuristics = self.CCHeuristics() if not heuristics else heuristics
 
     @property
     def dgst(self) -> str:
@@ -337,6 +384,9 @@ class CommonCriteriaCert(
 
     @property
     def eal(self) -> Optional[str]:
+        """
+        Returns EAL of certificate if it was extracted, None otherwise.
+        """
         res = [x for x in self.security_level if re.match(security_level_csv_scan, x)]
         if not res:
             return None
@@ -368,6 +418,9 @@ class CommonCriteriaCert(
 
     @property
     def pandas_tuple(self) -> Tuple:
+        """
+        Returns tuple of attributes meant for pandas serialization
+        """
         return (
             self.dgst,
             self.heuristics.cert_id,
@@ -398,7 +451,7 @@ class CommonCriteriaCert(
         printed_manufacturer = self.manufacturer if self.manufacturer else "Unknown manufacturer"
         return str(printed_manufacturer) + " " + str(self.name) + " dgst: " + self.dgst
 
-    def merge(self, other: "CommonCriteriaCert", other_source: Optional[str] = None) -> None:
+    def merge(self, other: CommonCriteriaCert, other_source: Optional[str] = None) -> None:
         """
         Merges with other CC sample. Assuming they come from different sources, e.g., csv and html.
         Assuming that html source has better protection profiles, they overwrite CSV info
@@ -425,7 +478,10 @@ class CommonCriteriaCert(
                     )
 
     @classmethod
-    def from_dict(cls, dct: Dict) -> "CommonCriteriaCert":
+    def from_dict(cls, dct: Dict) -> CommonCriteriaCert:
+        """
+        Deserializes dictionary into `CommonCriteriaCert`
+        """
         new_dct = dct.copy()
         new_dct["maintenance_updates"] = set(dct["maintenance_updates"])
         new_dct["protection_profiles"] = set(dct["protection_profiles"])
@@ -508,7 +564,7 @@ class CommonCriteriaCert(
         return None
 
     @staticmethod
-    def _html_row_get_maintenance_updates(main_div: Tag) -> Set["CommonCriteriaCert.MaintenanceReport"]:
+    def _html_row_get_maintenance_updates(main_div: Tag) -> Set[CommonCriteriaCert.MaintenanceReport]:
         possible_updates = list(main_div.find_all("li"))
         maintenance_updates = set()
         for u in possible_updates:
@@ -531,9 +587,9 @@ class CommonCriteriaCert(
         return maintenance_updates
 
     @classmethod
-    def from_html_row(cls, row: Tag, status: str, category: str) -> "CommonCriteriaCert":
+    def from_html_row(cls, row: Tag, status: str, category: str) -> CommonCriteriaCert:
         """
-        Creates a CC sample from html row
+        Creates a CC sample from html row of commoncriteria.org webpage.
         """
 
         cells = list(row.find_all("td"))
@@ -582,6 +638,14 @@ class CommonCriteriaCert(
         report_txt_dir: Optional[Union[str, Path]],
         st_txt_dir: Optional[Union[str, Path]],
     ) -> None:
+        """
+        Sets paths to files given the requested directories
+
+        :param Optional[Union[str, Path]] report_pdf_dir: Directory where pdf reports shall be stored
+        :param Optional[Union[str, Path]] st_pdf_dir: Directory where pdf security targets shall be stored
+        :param Optional[Union[str, Path]] report_txt_dir: Directory where txt reports shall be stored
+        :param Optional[Union[str, Path]] st_txt_dir: Directory where txt security targets shall be stored
+        """
         if report_pdf_dir is not None:
             self.state.report_pdf_path = Path(report_pdf_dir) / (self.dgst + ".pdf")
         if st_pdf_dir is not None:
@@ -592,7 +656,13 @@ class CommonCriteriaCert(
             self.state.st_txt_path = Path(st_txt_dir) / (self.dgst + ".txt")
 
     @staticmethod
-    def download_pdf_report(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def download_pdf_report(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Downloads pdf of certification report given the certificate. Staticmethod to allow for parallelization.
+
+        :param CommonCriteriaCert cert: cert to download the pdf report for
+        :return CommonCriteriaCert: returns the modified certificate with updated state
+        """
         exit_code: Union[str, int]
         if not cert.report_link:
             exit_code = "No link"
@@ -606,7 +676,13 @@ class CommonCriteriaCert(
         return cert
 
     @staticmethod
-    def download_pdf_target(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def download_pdf_st(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Downloads pdf of security target given the certificate. Staticmethod to allow for parallelization.
+
+        :param CommonCriteriaCert cert: cert to download the pdf security target for
+        :return CommonCriteriaCert: returns the modified certificate with updated state
+        """
         exit_code: Union[str, int]
         if not cert.st_link:
             exit_code = "No link"
@@ -620,7 +696,13 @@ class CommonCriteriaCert(
         return cert
 
     @staticmethod
-    def convert_report_pdf(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def convert_report_pdf(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Converts the pdf certification report to txt, given the certificate. Staticmethod to allow for parallelization.
+
+        :param CommonCriteriaCert cert: cert to download the pdf report for
+        :return CommonCriteriaCert: the modified certificate with updated state
+        """
         exit_code = helpers.convert_pdf_file(cert.state.report_pdf_path, cert.state.report_txt_path)
         if exit_code != constants.RETURNCODE_OK:
             error_msg = "failed to convert report pdf->txt"
@@ -630,7 +712,13 @@ class CommonCriteriaCert(
         return cert
 
     @staticmethod
-    def convert_target_pdf(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def convert_st_pdf(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Converts the pdf security target to txt, given the certificate. Staticmethod to allow for parallelization.
+
+        :param CommonCriteriaCert cert: cert to download the pdf security target for
+        :return CommonCriteriaCert: the modified certificate with updated state
+        """
         exit_code = helpers.convert_pdf_file(cert.state.st_pdf_path, cert.state.st_txt_path)
         if exit_code != constants.RETURNCODE_OK:
             error_msg = "failed to convert security target pdf->txt"
@@ -640,7 +728,13 @@ class CommonCriteriaCert(
         return cert
 
     @staticmethod
-    def extract_st_pdf_metadata(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def extract_st_pdf_metadata(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Extracts metadata from security target pdf given the certificate. Staticmethod to allow for parallelization.
+
+        :param CommonCriteriaCert cert: cert to extract the metadata for.
+        :return CommonCriteriaCert: the modified certificate with updated state
+        """
         response, cert.pdf_data.st_metadata = helpers.extract_pdf_metadata(cert.state.st_pdf_path)
         if response != constants.RETURNCODE_OK:
             cert.state.st_extract_ok = False
@@ -648,7 +742,13 @@ class CommonCriteriaCert(
         return cert
 
     @staticmethod
-    def extract_report_pdf_metadata(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def extract_report_pdf_metadata(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Extracts metadata from certification report pdf given the certificate. Staticmethod to allow for parallelization.
+
+        :param CommonCriteriaCert cert: cert to extract the metadata for.
+        :return CommonCriteriaCert: the modified certificate with updated state
+        """
         response, cert.pdf_data.report_metadata = helpers.extract_pdf_metadata(cert.state.report_pdf_path)
         if response != constants.RETURNCODE_OK:
             cert.state.report_extract_ok = False
@@ -656,7 +756,13 @@ class CommonCriteriaCert(
         return cert
 
     @staticmethod
-    def extract_st_pdf_frontpage(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def extract_st_pdf_frontpage(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Extracts data from security target pdf frontpage given the certificate. Staticmethod to allow for parallelization.
+
+        :param CommonCriteriaCert cert: cert to extract the frontpage data for.
+        :return CommonCriteriaCert: the modified certificate with updated state
+        """
         cert.pdf_data.st_frontpage = {}
 
         for header_type, associated_header_func in HEADERS.items():
@@ -671,7 +777,13 @@ class CommonCriteriaCert(
         return cert
 
     @staticmethod
-    def extract_report_pdf_frontpage(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def extract_report_pdf_frontpage(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Extracts data from certification report pdf frontpage given the certificate. Staticmethod to allow for parallelization.
+
+        :param CommonCriteriaCert cert: cert to extract the frontpage data for.
+        :return CommonCriteriaCert: the modified certificate with updated state
+        """
         cert.pdf_data.report_frontpage = {}
 
         for header_type, associated_header_func in HEADERS.items():
@@ -686,14 +798,28 @@ class CommonCriteriaCert(
         return cert
 
     @staticmethod
-    def extract_report_pdf_keywords(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def extract_report_pdf_keywords(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Matches regular expresions in txt obtained from certification report and extracts the matches into attribute.
+        Static method to allow for parallelization
+
+        :param CommonCriteriaCert cert: certificate to extract the keywords for.
+        :return CommonCriteriaCert: the modified certificate with extracted keywords.
+        """
         response, cert.pdf_data.report_keywords = helpers.extract_keywords(cert.state.report_txt_path)
         if response != constants.RETURNCODE_OK:
             cert.state.report_extract_ok = False
         return cert
 
     @staticmethod
-    def extract_st_pdf_keywords(cert: "CommonCriteriaCert") -> "CommonCriteriaCert":
+    def extract_st_pdf_keywords(cert: CommonCriteriaCert) -> CommonCriteriaCert:
+        """
+        Matches regular expresions in txt obtained from security target and extracts the matches into attribute.
+        Static method to allow for parallelization
+
+        :param CommonCriteriaCert cert: certificate to extract the keywords for.
+        :return CommonCriteriaCert: the modified certificate with extracted keywords.
+        """
         response, cert.pdf_data.st_keywords = helpers.extract_keywords(cert.state.st_txt_path)
         if response != constants.RETURNCODE_OK:
             cert.state.st_extract_ok = False
@@ -701,15 +827,26 @@ class CommonCriteriaCert(
         return cert
 
     def compute_heuristics_version(self) -> None:
+        """
+        Fills in the heuristically obtained version of certified product into attribute in heuristics class.
+        """
         self.heuristics.extracted_versions = helpers.compute_heuristics_version(self.name) if self.name else set()
 
     def compute_heuristics_cert_lab(self) -> None:
+        """
+        Fills in the heuristically obtained evaluation laboratory into attribute in heuristics class.
+        """
         if not self.pdf_data:
             logger.error("Cannot compute sample lab when pdf files were not processed.")
             return
         self.heuristics.cert_lab = self.pdf_data.cert_lab
 
     def compute_heuristics_cert_id(self, all_cert_ids: Set[str]):
+        """
+        Given list of cert ids from the whole dataset, will normalize own cert id into canonical form
+
+        :param Set[str] all_cert_ids: cert ids from the whole dataset.
+        """
         if not self.pdf_data:
             logger.warning("Cannot compute sample id when pdf files were not processed.")
             return
@@ -729,6 +866,7 @@ class CommonCriteriaCert(
 
         # Bug - getting out of index - ANSSI-2009/30
         # TMP solution
+        # TODO: Fix me, @georgefi
         if len(new_cert_id) >= len("ANSSI-CC-0000") + 1:
             if (
                 new_cert_id[len("ANSSI-CC-0000")] == "_"
@@ -821,7 +959,7 @@ class CommonCriteriaCert(
 
         return new_cert_id
 
-    def get_cert_laboratory(self) -> str:
+    def _get_cert_laboratory(self) -> str:
         if not self.heuristics.cert_id:
             raise ValueError("Cert ID was None but cert laboratory was to be computed based on its value.")
         cert_id = self.heuristics.cert_id.strip()
@@ -841,6 +979,12 @@ class CommonCriteriaCert(
         return "unknown"
 
     def normalize_cert_id(self, all_cert_ids: Set[str]) -> None:
+        """
+        Attempts to find certification laboratory and transform certificate id into canonical form. This is achieved
+        also by comparisons to all other cert ids in the dataset.
+
+        :param Set[str] all_cert_ids: set of all cert ids in the dataset.
+        """
         fix_methods: Dict[str, Callable] = {
             "anssi": CommonCriteriaCert._fix_anssi_cert_id,
             "bsi": partial(CommonCriteriaCert._fix_bsi_cert_id, all_cert_ids=all_cert_ids),
@@ -849,7 +993,7 @@ class CommonCriteriaCert(
         }
 
         try:
-            cert_lab = self.get_cert_laboratory()
+            cert_lab = self._get_cert_laboratory()
         except ValueError:
             return None
 
diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py
index 0b769dec..b2680f0c 100644
--- a/sec_certs/sample/fips.py
+++ b/sec_certs/sample/fips.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import copy
 import re
 from dataclasses import dataclass, field
@@ -22,6 +24,13 @@ from sec_certs.serialization.json import ComplexSerializableType
 
 
 class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuristics"], ComplexSerializableType):
+    """
+    Data structure for common FIPS 140 certificate. Contains several inner classes that layer the data logic.
+    Can be serialized into/from json (`ComplexSerializableType`).
+    Is basic element of `FIPSDataset`. The functionality is mostly related to holding data and transformations that
+    the certificate can handle itself. `FIPSDataset` class then instrument this functionality.
+    """
+
     FIPS_BASE_URL: ClassVar[str] = "https://csrc.nist.gov"
     FIPS_MODULE_URL: ClassVar[
         str
@@ -29,6 +38,10 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @dataclass(eq=True)
     class State(ComplexSerializableType):
+        """
+        Holds state of the `FIPSCertificate`
+        """
+
         sp_path: Path
         html_path: Path
         fragment_path: Path
@@ -67,6 +80,10 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @dataclass(eq=True)
     class Algorithm(ComplexSerializableType):
+        """
+        Data structure for algorithm of `FIPSCertificate`
+        """
+
         cert_id: str
         vendor: str
         implementation: str
@@ -87,6 +104,10 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @dataclass(eq=True)
     class WebScan(ComplexSerializableType):
+        """
+        Data structure for data obtained from scanning certificate webpage at NIST.gov
+        """
+
         module_name: Optional[str]
         standard: Optional[str]
         status: Optional[str]
@@ -147,6 +168,10 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @dataclass(eq=True)
     class PdfScan(ComplexSerializableType):
+        """
+        Data structure that holds data obtained from scanning pdf files (or their converted txt documents).
+        """
+
         cert_id: int
         keywords: Dict
         algorithms: List
@@ -165,6 +190,10 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @dataclass(eq=True)
     class FIPSHeuristics(Heuristics, ComplexSerializableType):
+        """
+        Data structure that holds data obtained by processing the certificate and applying various heuristics.
+        """
+
         keywords: Dict[str, Dict]
         algorithms: List[Dict[str, Dict]]
         unmatched_algs: int
@@ -192,6 +221,9 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @property
     def dgst(self) -> str:
+        """
+        Returns primary key of the certificate, its id.
+        """
         return fips_dgst(self.cert_id)
 
     # TODO: Fix type errors, they exist because FIPS uses this as property to change variable names, while CC and abstract class have variables
@@ -221,6 +253,9 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @staticmethod
     def download_security_policy(cert: Tuple[str, Path]) -> None:
+        """
+        Downloads security policy file from web. Staticmethod to allow for parametrization.
+        """
         exit_code = helpers.download_file(*cert, delay=1)
         if exit_code != requests.codes.ok:
             logger.error(f"Failed to download security policy from {cert[0]}, code: {exit_code}")
@@ -228,20 +263,26 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
     def __init__(
         self,
         cert_id: int,
-        web_scan: "FIPSCertificate.WebScan",
-        pdf_scan: "FIPSCertificate.PdfScan",
-        heuristics: "FIPSCertificate.FIPSHeuristics",
+        web_scan: FIPSCertificate.WebScan,
+        pdf_scan: FIPSCertificate.PdfScan,
+        heuristics: FIPSCertificate.FIPSHeuristics,
         state: State,
     ):
         super().__init__()
         self.cert_id = cert_id
         self.web_scan = web_scan
         self.pdf_scan = pdf_scan
-        self.heuristics: "FIPSCertificate.FIPSHeuristics" = heuristics
+        self.heuristics: FIPSCertificate.FIPSHeuristics = heuristics
         self.state = state
 
     @classmethod
-    def from_dict(cls, dct: Dict) -> "FIPSCertificate":
+    def from_dict(cls, dct: Dict) -> FIPSCertificate:
+        """
+        Deserializes dictionary into FIPSCertificate
+
+        :param Dict dct: dictionary that holds the FIPSCertificate data
+        :return FIPSCertificate: object reconstructed from dct
+        """
         new_dct = dct.copy()
 
         if new_dct["web_scan"].date_validation:
@@ -253,6 +294,12 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @staticmethod
     def download_html_page(cert: Tuple[str, Path]) -> Optional[Tuple[str, Path]]:
+        """
+        Wrapper for downloading a file. `delay=1` introduced to avoid problems with requests at NIST.gov
+
+        :param Tuple[str, Path] cert: tuple url, output_path
+        :return Optional[Tuple[str, Path]]: None on success, `cert` on failure.
+        """
         exit_code = helpers.download_file(*cert, delay=1)
         if exit_code != requests.codes.ok:
             logger.error(f"Failed to download html page from {cert[0]}, code: {exit_code}")
@@ -260,7 +307,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         return None
 
     @staticmethod
-    def initialize_dictionary() -> Dict[str, Any]:
+    def _initialize_dictionary() -> Dict[str, Any]:
         return {
             "module_name": None,
             "standard": None,
@@ -296,8 +343,9 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
     def parse_caveat(current_text: str) -> Dict[str, Dict[str, int]]:
         """
         Parses content of "Caveat" of FIPS CMVP .html file
-        :param current_text: text of "Caveat"
-        :return: dictionary of all found algorithm IDs
+
+        :param str current_text: text of "Caveat"
+        :return Dict[str, Dict[str, int]]: dictionary of all found algorithm IDs
         """
         ids_found: Dict[str, Dict[str, int]] = {}
         r_key = r"(?P<word>\w+)?\s?(?:#\s?|Cert\.?(?!.\s)\s?|Certificate\s?)+(?P<id>\d+)"
@@ -315,9 +363,10 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
     def extract_algorithm_certificates(current_text: str, in_pdf: bool = False) -> List[Optional[Dict[str, List[str]]]]:
         """
         Parses table of FIPS (non) allowed algorithms
-        :param current_text: Contents of the table
-        :param in_pdf: Specifies whether the table was found in a PDF security policies file
-        :return: List containing one element - dictionary with all parsed algorithm cert ids
+
+        :param str current_text: Contents of the table
+        :param bool in_pdf: Specifies whether the table was found in a PDF security policies file, defaults to False
+        :return List[Optional[Dict[str, List[str]]]]: List containing one element - dictionary with all parsed algorithm cert ids
         """
         set_items = set()
         if in_pdf:
@@ -333,9 +382,11 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
     def parse_table(element: Union[Tag, NavigableString]) -> List[Dict[str, Any]]:
         """
         Parses content of <table> tags in FIPS .html CMVP page
-        :param element: text in <table> tags
-        :return: list of all found algorithm IDs
+
+        :param Union[Tag, NavigableString] element: text in <table> tags
+        :return List[Dict[str, Any]]: list of all found algorithm IDs
         """
+
         found_items = []
         trs = element.find_all("tr")
         for tr in trs:
@@ -355,7 +406,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         return found_items
 
     @staticmethod
-    def parse_html_main(current_div: Tag, html_items_found: Dict, pairs: Dict[str, str]) -> None:
+    def _parse_html_main(current_div: Tag, html_items_found: Dict, pairs: Dict[str, str]) -> None:
         title = current_div.find("div", class_="col-md-3").text.strip()
         content = (
             current_div.find("div", class_="col-md-9")
@@ -391,7 +442,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
                 html_items_found[pairs[title]] = content
 
     @staticmethod
-    def parse_vendor(current_div: Tag, html_items_found: Dict, current_file: Path) -> None:
+    def _parse_vendor(current_div: Tag, html_items_found: Dict, current_file: Path) -> None:
         vendor_string = current_div.find("div", "panel-body").find("a")
 
         if not vendor_string:
@@ -406,7 +457,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
             logger.warning(f"NO VENDOR FOUND {current_file}")
 
     @staticmethod
-    def parse_lab(current_div: Tag, html_items_found: Dict, current_file: Path) -> None:
+    def _parse_lab(current_div: Tag, html_items_found: Dict, current_file: Path) -> None:
         html_items_found["lab"] = list(current_div.find("div", "panel-body").children)[0].strip()
         html_items_found["nvlap_code"] = (
             list(current_div.find("div", "panel-body").children)[2].strip().split("\n")[1].strip()
@@ -427,20 +478,29 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
             html_items_found["certificate_www"] = constants.FIPS_BASE_URL + links[1].get("href")
 
     @staticmethod
-    def normalize(items: Dict) -> None:
+    def _normalize(items: Dict) -> None:
         items["module_type"] = items["module_type"].lower().replace("-", " ").title()
         items["embodiment"] = items["embodiment"].lower().replace("-", " ").replace("stand alone", "standalone").title()
 
     @staticmethod
-    def parse_validation_dates(current_div: Tag, html_items_found: Dict) -> None:
+    def _parse_validation_dates(current_div: Tag, html_items_found: Dict) -> None:
         table = current_div.find("table")
         rows = table.find("tbody").findAll("tr")
         html_items_found["date_validation"] = [parser.parse(td.text).date() for td in [row.find("td") for row in rows]]
 
     @classmethod
-    def html_from_file(
-        cls, file: Path, state: State, initialized: "FIPSCertificate" = None, redo: bool = False
-    ) -> "FIPSCertificate":
+    def from_html_file(
+        cls, file: Path, state: State, initialized: FIPSCertificate = None, redo: bool = False
+    ) -> FIPSCertificate:
+        """
+        Constructs FIPSCertificate object from html file.
+
+        :param Path file: path to the html file to use for initialization
+        :param State state: state of the certificate
+        :param FIPSCertificate initialized: possibly partially initialized FIPSCertificate, defaults to None
+        :param bool redo: if the method should be reattempted in case of failure, defaults to False
+        :return FIPSCertificate: resulting `FIPSCertificate` object.
+        """
         pairs = {
             "Module Name": "module_name",
             "Standard": "standard",
@@ -466,7 +526,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
             "Product URL": "product_url",
         }
         if not initialized:
-            items_found = FIPSCertificate.initialize_dictionary()
+            items_found = FIPSCertificate._initialize_dictionary()
             items_found["cert_id"] = int(file.stem)
         else:
             items_found = initialized.web_scan.__dict__
@@ -479,28 +539,28 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
             state.txt_state = initialized.state.txt_state
 
         if redo:
-            items_found = FIPSCertificate.initialize_dictionary()
+            items_found = FIPSCertificate._initialize_dictionary()
             items_found["cert_id"] = int(file.stem)
 
         text = helpers.load_cert_html_file(str(file))
         soup = BeautifulSoup(text, "html.parser")
         for div in soup.find_all("div", class_="row padrow"):
-            FIPSCertificate.parse_html_main(div, items_found, pairs)
+            FIPSCertificate._parse_html_main(div, items_found, pairs)
 
         for div in soup.find_all("div", class_="panel panel-default")[1:]:
             if div.find("h4", class_="panel-title").text == "Vendor":
-                FIPSCertificate.parse_vendor(div, items_found, file)
+                FIPSCertificate._parse_vendor(div, items_found, file)
 
             if div.find("h4", class_="panel-title").text == "Lab":
-                FIPSCertificate.parse_lab(div, items_found, file)
+                FIPSCertificate._parse_lab(div, items_found, file)
 
             if div.find("h4", class_="panel-title").text == "Related Files":
                 FIPSCertificate.parse_related_files(div, items_found)
 
             if div.find("h4", class_="panel-title").text == "Validation History":
-                FIPSCertificate.parse_validation_dates(div, items_found)
+                FIPSCertificate._parse_validation_dates(div, items_found)
 
-        FIPSCertificate.normalize(items_found)
+        FIPSCertificate._normalize(items_found)
 
         return FIPSCertificate(
             items_found["cert_id"],
@@ -543,7 +603,13 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         )
 
     @staticmethod
-    def convert_pdf_file(tup: Tuple["FIPSCertificate", Path, Path]) -> "FIPSCertificate":
+    def convert_pdf_file(tup: Tuple[FIPSCertificate, Path, Path]) -> FIPSCertificate:
+        """
+        Converts pdf file of FIPSCertificate. Staticmethod to allow for paralelization.
+
+        :param Tuple[FIPSCertificate, Path, Path] tup: object which file will be converted, path to pdf, path to txt.
+        :return FIPSCertificate: the modified FIPSCertificate.
+        """
         cert, pdf_path, txt_path = tup
         if not cert.state.txt_state:
             exit_code = helpers.convert_pdf_file(pdf_path, txt_path)
@@ -565,7 +631,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         return len(text) * 0.5 <= len("".join(filter(str.isalpha, text)))
 
     @staticmethod
-    def find_keywords(cert: "FIPSCertificate") -> Tuple[Optional[Dict], "FIPSCertificate"]:
+    def find_keywords(cert: FIPSCertificate) -> Tuple[Optional[Dict], FIPSCertificate]:
         if not cert.state.txt_state:
             return None, cert
 
@@ -580,11 +646,11 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         if config.ignore_first_page:
             text_to_parse = text_to_parse[text_to_parse.index("") :]
 
-        items_found, fips_text = FIPSCertificate.parse_cert_file(FIPSCertificate.remove_platforms(text_to_parse))
+        items_found, fips_text = FIPSCertificate._parse_cert_file(FIPSCertificate._remove_platforms(text_to_parse))
 
         save_modified_cert_file(cert.state.fragment_path.with_suffix(".fips.txt"), fips_text, unicode_error)
 
-        common_items_found, common_text = FIPSCertificate.parse_cert_file_common(
+        common_items_found, common_text = FIPSCertificate._parse_cert_file_common(
             text_to_parse, text_with_newlines, fips_common_rules
         )
 
@@ -594,7 +660,13 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         return items_found, cert
 
     @staticmethod
-    def match_web_algs_to_pdf(cert: "FIPSCertificate") -> int:
+    def match_web_algs_to_pdf(cert: FIPSCertificate) -> int:
+        """
+        Finds algorithms in FIPSCertificate. Staticmethod to allow for parallelization.
+
+        :param FIPSCertificate cert: cert to search for algorithms.
+        :return int: number of identified algorithms.
+        """
         algs_vals = list(cert.pdf_scan.keywords["rules_fips_algorithms"].values())
         table_vals = [x["Certificate"] for x in cert.pdf_scan.algorithms]
         tables = [x.strip() for y in table_vals for x in y]
@@ -619,7 +691,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         return len(not_found)
 
     @staticmethod
-    def remove_platforms(text_to_parse: str) -> str:
+    def _remove_platforms(text_to_parse: str) -> str:
         pat = re.compile(r"(?:(?:modification|revision|change) history|version control)\n[\s\S]*?", re.IGNORECASE)
         for match in pat.finditer(text_to_parse):
             text_to_parse = text_to_parse.replace(match.group(), "x" * len(match.group()))
@@ -669,7 +741,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
             items_found[rule_str][match][constants.TAG_MATCH_MATCHES].append([match_span[0], match_span[1]])
 
     @staticmethod
-    def parse_cert_file_common(
+    def _parse_cert_file_common(
         text_to_parse: str, whole_text_with_newlines: str, search_rules: Dict
     ) -> Tuple[Dict[Pattern, Dict], str]:
         # apply all rules
@@ -699,7 +771,7 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         return items_found_all, whole_text_with_newlines
 
     @staticmethod
-    def parse_cert_file(text_to_parse: str) -> Tuple[Dict[Pattern, Dict], str]:
+    def _parse_cert_file(text_to_parse: str) -> Tuple[Dict[Pattern, Dict], str]:
         # apply all rules
         items_found_all: Dict = {}
 
@@ -733,7 +805,13 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
         return items_found_all, text_to_parse
 
     @staticmethod
-    def analyze_tables(tup: Tuple["FIPSCertificate", bool]) -> Tuple[bool, "FIPSCertificate", List]:
+    def analyze_tables(tup: Tuple[FIPSCertificate, bool]) -> Tuple[bool, FIPSCertificate, List]:
+        """
+        Searches for tables in pdf documents of the instance.
+
+        :param Tuple[FIPSCertificate, bool] tup: certificate object, whether to use high precision results or approx. results
+        :return Tuple[bool, FIPSCertificate, List]: True on success / False otherwise, modified cert object, List of processed tables.
+        """
         cert, precision = tup
         if (not precision and cert.state.tables_done) or (
             precision and cert.heuristics.unmatched_algs < config.cert_threshold
@@ -801,6 +879,9 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
                     to_pop.add(cert)
 
     def remove_algorithms(self) -> None:
+        """
+        Removes algorithms from the certificate.
+        """
         self.state.file_status = True
         if not self.pdf_scan.keywords:
             return
@@ -829,12 +910,18 @@ class FIPSCertificate(Certificate["FIPSCertificate", "FIPSCertificate.FIPSHeuris
 
     @staticmethod
     def get_compare(vendor: str) -> str:
+        """
+        Tokenizes vendor name of the certificate.
+        """
         vendor_split = (
             vendor.replace(",", "").replace("-", " ").replace("+", " ").replace("®", "").replace("(R)", "").split()
         )
         return vendor_split[0][:4] if len(vendor_split) > 0 else vendor
 
     def compute_heuristics_version(self) -> None:
+        """
+        Heuristically computes the version of the product.
+        """
         versions_for_extraction = ""
         if self.web_scan.module_name:
             versions_for_extraction += f" {self.web_scan.module_name}"
diff --git a/sec_certs/serialization/json.py b/sec_certs/serialization/json.py
index 4799e259..1664f799 100644
--- a/sec_certs/serialization/json.py
+++ b/sec_certs/serialization/json.py
@@ -1,6 +1,7 @@
 import copy
 import json
 from datetime import date
+from functools import wraps
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union
 
@@ -55,6 +56,7 @@ class ComplexSerializableType:
 
 # Decorator for serialization
 def serialize(func: Callable):
+    @wraps(func)
     def inner_func(*args, **kwargs):
         if not args or not issubclass(type(args[0]), ComplexSerializableType):
             raise ValueError(
diff --git a/setup.py b/setup.py
index 802dc9c9..298de435 100644
--- a/setup.py
+++ b/setup.py
@@ -5,8 +5,6 @@ setup(
     name="sec-certs",
     author="Petr Svenda, Stanislav Bobon, Jan Jancar, Adam Janovsky, Jiri Michalik",
     author_email="svenda@fi.muni.cz",
-    version_config=True,
-    setup_requires=["setuptools-git-versioning"],
     packages=find_packages(),
     license="MIT",
     description="Tool for analysis of security certificates",
@@ -32,4 +30,9 @@ setup(
     include_package_data=True,
     package_data={"sec_certs": ["settings.yaml", "settings-schema.json"]},
     entry_points={"console_scripts": ["cc-certs=cc_cli:main", "fips-certs=fips_cli:main"]},
+    project_urls={
+        "Project homepage": "https://seccerts.org",
+        "GitHub repository": "https://github.com/crocs-muni/sec-certs/",
+        "Documentation": "https://seccerts.org/docs",
+    },
 )
diff --git a/tests/test_cc_oop.py b/tests/test_cc_oop.py
index 213d41bd..479d3b05 100644
--- a/tests/test_cc_oop.py
+++ b/tests/test_cc_oop.py
@@ -222,7 +222,7 @@ class TestCommonCriteriaOOP(TestCase):
         with TemporaryDirectory() as tmp_dir:
             dataset_path = Path(tmp_dir)
             dset = CCDataset({}, dataset_path, "sample_dataset", "sample dataset description")
-            dset.download_csv_html_resources(get_active=True, get_archived=False)
+            dset._download_csv_html_resources(get_active=True, get_archived=False)
 
             for x in dset.active_html_tuples:
                 self.assertTrue(x[1].exists())