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Gender Diversity Analysis in OSS Projects
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Bitergia
June 29, 2017
Technology
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Gender Diversity Analysis in OSS Projects
PyData Meetup, Madrid
Bitergia
June 29, 2017
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Transcript
Gender Diversity Analysis in OSS Projects PyData Meetup 29th June,
2017 Madrid Daniel Izquierdo, CDO
[email protected]
@dizquierdo speakerdeck.com/bitergia
None
None
Tweet => 13% of people attending the OpenStack Summit were
women Tweet => How many of them are actually contributing to the source code? Intro
Gender diversity of the technical contributions in the OpenStack Project
Teams Problem
Goal-Question-Metric approach • Contextualize tech gender-diversity groups • Data sources
available for the analysis • Tooling • Results and Further Work How To
Governance -> Goals <- Questions <- Metrics Goal: Increase gender
diversity in the OpenStack Foundation Context
How’s performing the industry with this respect? Context
FOSS Survey in 2013: - 11% of women answered the
survey The Industry Gender Gap by the World Economic Forum. - 5% for CEOs, 21% for Mid-level roles, 32% of Junior roles Context
Some companies https://blog.pinterest.com/en/our -plan-more-diverse-pinterest http://www.google.com/diversity/ http://newsroom.fb.com/news/2015/0 6/driving-diversity-at-facebook/ https://blogs.dropbox.com/dropbox/2014/11/stren gthening-dropbox-through-diversity/
Question: How’s performing OpenStack? We need data!!!! Context
Git: https://git.openstack.org Gerrit: https://review.openstack.org/ Others: Mailing Lists, Launchpad, IRC… >1M
commits and > 1.5M code review votes Data
Git example: commit 61ab0c46b09299c07e86320d612a0fcc281491b1 Author: Daniel Izquierdo <
[email protected]
> Date: Fri
Apr 28 00:04:05 2017 +0200 Add 'by default' values when eventizing Data
Tooling Original Data Sources Mining Tools Perceval @ GrimoireLab Info
Enrich. Genderize.io Ceres/ Pandas Jupyter Notebooks Manual work Viz ElasticSearch + Kibana
Results Original Data Sources • Git and Gerrit repos based
on yaml at Governance • ~ 1M commits • ~ 500K changesets • ~ 1.5M patchset uploads • ~ 1.8M patches code reviews
Results Mining Tools Perceval • At grimoirelab.github.io • Parses API’s,
logs, etc and produces JSON documents • Those are later stored in ElasticSearch
Results Info Enrich. Genderize.io Pandas Jupyter Notebooks Manual work •
Genderize.io: name database • Ceres: data analysis lib. to work with Perceval • Jupyter Notebook: web app. For data analysis • Manual work:
Results Viz ElasticSearch + Kibana • ElasticSearch: Schemaless db •
Kibana: works great with ES • This tandem helps a lot to verify info • Drill down capabilities
Demo OpenStack Diversity Dashboard (private access)
Women activity (last year): ~ 11% of the population (
~ 340 active developers ) ~ 9% of the activity ( >=6k commits ) OpenStack (Austin)
Women activity (last year): ~ 6.8% of the activity (
~ 4k commits ) ~ 9.9% of the population ( ~ 330 active developers ) Linux Kernel
Women activity (last year): ~2K commits (6.5% of the activity)
71 developers (8.5% of the population) Hadoop
Users It’s important to understand your potential users! C-level? Middle
management? Developers? Community? This study aims at understanding the current situation And look for best practices
Decisions based on data!
Bitergia Software Development Analytics for your peace of mind
Thanks! Daniel Izquierdo, CDO
[email protected]
@dizquierdo