Gender-diversity analysis of technical contributions Daniel Izquierdo Cortázar @dizquierdo dizquierdo at bitergia dot com OpenStack Summit, Austin 2016
/me CDO in Bitergia, the software development analytics company Involved in the OpenStack Activity Board Involved in the OpenStack Quarterly Reports Disclaimer: not involved in WOO working group, own analysis and interest, I may have missed some stuff...
Why this study Diversity matters I attended some WOO (Women of OpenStack) talks in Tokyo There are not numbers about technical contributions (AFAIK) How’s this evolving? Is gender-diversity increasing? In the end this is all about transparency and improvement
What we have so far OpenStack related resources: - Linkedin WOO: 600 members, 137 discussions - WOO mailing list: 380 emails, 140 threads, 90 participants - WOO wiki
What we have so far Others of interest: FOSS Survey in 2013: - http://floss2013.libresoft.es/results.en.html - 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
Summary Conclusions not representative, but: - Women represents around 30%/40% of the workforce in tech companies. - And between 10% and 20% if focused on tech teams. - What about OpenStack?
Some Definitions Technical contributions: commit, upload, gerrit vote Other potential metrics: diversity by company, fairness in the code review among organizations and genders, transparency in the process Available but sensitive info: affiliation, countries, time to review
Architecture Original Data Sources Mining Tools CVSAnalY Bicho SortingHat Info Enrich. Genderize.io Pandas Jupyter Notebooks Manual work Viz ElasticSearch + Kibana
Architecture Mining Tools CVSAnalY Bicho SortingHat ● CVSAnalY and Bicho ddbb publicly available ● Activity Board (http://activity.openstack.org/dash/browser/data/db/ ) ● SortingHat db available under request (sensitive info) ● Bonus: now migrating to GrimoireLab
Architecture Info Enrich. Genderize.io Pandas Jupyter Notebooks Manual work ● Genderize.io: name database ● Pandas: data analysis lib. ● Jupyter Notebook: web app. For data analysis ● Manual work:
Architecture Viz ElasticSearch + Kibana ● ElasticSearch: Schemaless db ● Kibana: works great with ES ● This tandem helps a lot to verify info ● Drill down capabilities ● Extra info available (but not displayed)
Validation: manual work Check main contributors by hand Be sure the WOO Wiki contributors are correct Asian names hard to check ( u_u ) (help needed!) Others...
Git Activity and Population Women activity (all of the history): ~ 10,5% of the population ( ~ 570 developers ) ~ 6,8% of the activity ( >=16k commits )
Git WOO Main Projects ● Where do WOO contributions go? ● No-filtered order: Infra, Nova, Neutron, Doc, QA ● Lots of activity in Doc, Infra, Neutron, Nova and Horizon
Git WOO Type of Contribution ● Where do WOO contributions go? ● No-filtered order: Infra, Nova, Neutron, Doc, QA ● Lots of activity in Doc, Infra, Neutron, Nova and Horizon
Git WOO Evolution ● Similar trend than the overall evolution ● But slightly better during the last year ● Peaks during March, June August and November 2015 (any clue?)
Git WOO Evolution (peaks) ● March 2015: Extra activity in Ironic ● June 2015: Extra activity in Doc and Puppet OpenStack ● August 2015: Extra activity in Infra and Doc ● November 2015: Extra activity in Doc and OpenStack Client
Gerrit Reviews Evolution by WOO And that jump is even higher when checking +2 reviews Up to 3 times the ‘+2’ activity from 2014 to 2015 (This behaviour does not follow the general trend)
Demographics Attraction of female developers to the community Peak on 2015 Q3 with 62 developers [chart measures the first contribution by each developer and groups by quarter]
Demographics Female developers leaving the community [active developer = at least a commit during the last year] [chart measures the last contribution by each developer and groups by quarter]
Demographics: extra bonus And the other way around: How good are we retaining developers that entered in 2013-Q1? (And who are they? Working for? Working at?) [19 attracted in 2013 Q1. 6 left in that quarter. 7 are still contributing. Another 6 left in other periods]
Outreachy “Outreachy helps people from groups underrepresented in free and open source software get involved” Is helping Outreachy to decrease the gender gap in OpenStack? How’s performing the community to retain these developers? And how’s the overall performance of the community retaining developers?
Outreachy More women are attracted and retained (also in relative numbers) thanks to organizations in OpenStack. Even though, some numbers from tech companies show a higher % of women. Is it worth exploring to invest other resources in companies to kindly let know about this? What about exploring high school focused actions? (prior degree studies) Disclaimer: Just some ideas!
Some Answers Continuous increase of activity and population (up to 11%) Outstanding increase in core review contributions Most of the women come as new orgs. join the Foundation Tooling is useful to have number, compare and make decisions
Conclusions Room for improvement of the dataset This provides some initial numbers about the current status Hopefully useful for the WOO working group and the project
Open Paths How this may help the challenges detailed by the WOO: - Close to 550 female developers (more than 200 with a 100% of probability) - Talk to them, send an email, let them participate, have meetings, ask for mentorships - Detection of new women entering the community, say hello! https://wiki.openstack.org/wiki/Women_of_OpenStack
Further Work Sensitive info: dashboard still private Extra analysis: time to merge fairness, companies women %, Outreachy follow ups, quarterly reports, updated data, specific policies ROI and others. This [hopefully] helps to have a better picture Looking for sponsors!
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