On the Relationship Between the Developer's Perceptible Race and Ethnicity and the Evaluation of Contributions in OSS
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Abstract (arXiv)
Open Source Software (OSS) projects are typically the result of collective efforts performed by developers with different backgrounds. Although the quality of developers' contributions should be the only factor influencing the evaluation of the contributions to OSS projects, recent studies have shown that diversity issues are correlated with the acceptance or rejection of developers' contributions. This paper assists this emerging state-of-the-art body on diversity research with the first empirical study that analyzes how developers' perceptible race and ethnicity relates to the evaluation of the contributions in OSS. We performed a large-scale quantitative study of OSS projects in GitHub. We extracted the developers' perceptible race and ethnicity from their names in GitHub using the Name-Prism tool and applied regression modeling of contributions (i.e, pull requests) data from GHTorrent and GitHub. We observed that among the developers whose perceptible race and ethnicity was captured by the tool, only 16.56% were perceptible as Non-White developers; contributions from perceptible White developers have about 6-10% higher odds of being accepted when compared to contributions from perceptible Non-White developers; and submitters with perceptible non-white races and ethnicities are more likely to get their pull requests accepted when the integrator is estimated to be from their same race and ethnicity rather than when the integrator is estimated to be White. Our initial analysis shows a low number of Non-White developers participating in OSS. Furthermore, the results from our regression analysis lead us to believe that there may exist differences between the evaluation of the contributions from different perceptible races and ethnicities. Thus, our findings reinforce the need for further studies on racial and ethnic diversity in software engineering to foster healthier OSS communities.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Apr 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3.
4 papers.
- 2023Comprehensive Evaluation of ChatGPT Reliability Through Multilingual Inquiriescited by 11
- 2021On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requestscited by 6
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requestscited by 3
- 2023Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projectscited by 2
- 2023Comprehensive Evaluation of ChatGPT Reliability Through Multilingual Inquiries16 Dec 2023
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requests13 Nov 2023
- 2023Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projects29 May 2023
- 2021On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests28 Oct 2021