anubisgit 0.7.0

Anubis, a CodeMetrics helper

Anubis, a codemetrics tool.

You can read more about anubisgit on the internal docummentation page page.

Work in progress

Today, Anubis is essentially the script timemachine.py. the time machine is a loop can spawn various versions of a code from its gitbase, and perform analysis on it

Installation

Anubis I under fast development, and we do not update PiPY very often. For a quick installation in a simplified virtual environment

python3 -m venv venv_anubisgit
source venv_anubisgit/bin/activate
python -m pip install 'anubisgit @ git+https://gitlab.com/cerfacs/anubisgit.git'

You can obiously install it from the sources available on gitlab.com

main Usage

For now , there is a minimal CLI, limited to the execution of the time machine of Anubis Type >anubisgit to see what is available :

 >anubisgit
Usage: anubisgit [OPTIONS] COMMAND [ARGS]...

  Package anubisgit v0.7.0

  ---------------    Anubis  --------------------

      You are now using the Command line interface of anubisgit package,     a
      set of tools created at CERFACS (https://cerfacs.fr).

      This is a python package currently installed in your python
      environement.

Options:
  --help  Show this message and exit.

Commands:
  anew          Create a default input file in current folder.
  authors-list  Retrieve authors list from joined_commits_monthly.json
  branches      Analyze branch health
  chronology    Code geology
  complexity    Analyse code health
  join-db       Join Anubis database
  timemachine   Build anubisgit database

If your new to anubisgit, you can find a quick how to tutorial there

Good practice

Althought anubisgit timemachine cli command won't let you start a run if your repository is not clean. It is highly recommended to git clone the repository you want to analyze and have a repository for your work repository to avoid any unwanted behaviour or file deletion during the timemachine process.

Most common troubleshoots

• Unclean repository
• .gitignore

Acknowledgements

This work has been initially supported by the COEC project which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 952181 .