Women’s Participation in Open Source Software: A Survey of the Literature

By Bianca Trinkenreich, Igor Scaliante Wiese, Anita Sarma, Marco Aurélio Gerosa, Igor Fabio Steinmacher. 2021

95 citations recorded by the source corpus.

Women are underrepresented in Open Source Software (OSS) projects, as a result of which, not only do women lose career and skill development opportunities, but the projects themselves suffer from a lack of diversity of perspectives. Practitioners and researchers need to understand more about the phenomenon; however, studies about women in open source are spread across multiple fields, including information systems, software engineering, and social science. This article systematically maps, aggregates, and synthesizes the state-of-the-art on women’s participation in OSS. It focuses on women contributors’ representation and demographics, how they contribute, their motivations and challenges, and strategies employed by communities to attract and retain women. We identified 51 articles (published between 2000 and 2021) that investigated women’s participation in OSS. We found evidence in these papers about who are the women who contribute, what motivates them to contribute, what types of contributions they make, challenges they face, and strategies proposed to support their participation. According to these studies, only about 5% of projects were reported to have women as core developers, and women authored less than 5% of pull-requests, but had similar or even higher rates of pull-request acceptances than men. Women make both code and non-code contributions, and their motivations to contribute include learning new skills, altruism, reciprocity, and kinship. Challenges that women face in OSS are mainly social, including lack of peer parity and non-inclusive communication from a toxic culture. We found 10 strategies reported in the literature, which we mapped to the reported challenges. Based on these results, we provide guidelines for future research and practice.

Subfields: Computer Science Applications, Information Systems, Sociology and Political Science, Social Sciences & Humanities

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Citations in the corpus

Evaluating Large Language Models Trained on Code (2021, background): “This could increase open-source developers’ incentive to maintain backward compatibility, which could pose challenges given that open-source projects are often under-resourced (Eghbal, 2020; Trinkenreich et al., 2021).”