Daniel Izquierdo

Women represents half of the population. The technological industry claims that around 30% of their work force are women. And that percentage decreases down to 20% when focusing on the tech. teams.

If we analyze open source communities, those hardly reach 10% of the population as this is the case of the OpenStack Foundation or the Linux Kernel.

This talk will show previous analysis and data publicly available with this respect, and an analysis of the Python community [1] to compare to.

In addition to that, this talk will provide the steps done up to having the data: retrieving, curating, cleaning and visualizing the information with Python. For this process GrimoireLab, and specifically Perceval [2] was used used for retrieving information. Perceval retrieves information from the usual data sources found in the open source world. Then Ceres [3] is a small library that handle Perceval's data. These are the basics of the technical infrastructure.

[1] https://github.com/python [2] https://github.com/chaoss/grimoirelab-perceval, [3] https://github.com/chaoss/grimoirelab-cereslib

This talk aims at providing a quick overview of the GrimoireLib library. This python-based piece of code provides a list of classes that help to analyze open source communities. This talk is intended for those interested in open source analytics and want to have a better understanding of the community and processes around their favourite open source projects. The talk will use ipython notebook recipes to follow the code.

GrimoireLib [1] is a python-based library that facilitates the analysis of open source communities. This is part of the Grimoire toolset, a set of tools that help to download, parse and analyze open source communities from several perspectives. Activity in the source code, mailing lists, ticketing systems are some of the data sources supported by this toolset.

This talk assumes the existence of a previous CVSAnalY retrieved database and will play with the several metrics and analysis available at GrimoireLib. Examples driven on ipython notebook will help to follow the talk. And specific analysis of some communities will be carried out. Among other metrics, commits from several perspectives will be calculated: branches, message log, type of file, organizations, module, and others.

[1] https://github.com/VizGrimoire/GrimoireLib