Including Everyone, Everywhere: Understanding Opportunities and Challenges of Geographic Gender-Inclusion in OSS
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Abstract (arXiv)
The gender gap is a significant concern facing the software industry as the development becomes more geographically distributed. Widely shared reports indicate that gender differences may be specific to each region. However, how complete can these reports be with little to no research reflective of the Open Source Software (OSS) process and communities software is now commonly developed in? Our study presents a multi-region geographical analysis of gender inclusion on GitHub. This mixed-methods approach includes quantitatively investigating differences in gender inclusion in projects across geographic regions and investigate these trends over time using data from contributions to 21,456 project repositories. We also qualitatively understand the unique experiences of developers contributing to these projects through a survey that is strategically targeted to developers in various regions worldwide. Our findings indicate that gender diversity is low across all parts of the world, with no substantial difference across regions. However, there has been statistically significant improvement in diversity worldwide since 2014, with certain regions such as Africa improving at faster pace. We also find that most motivations and barriers to contributions (e.g., lack of resources to contribute and poor working environment) were shared across regions, however, some insightful differences, such as how to make projects more inclusive, did arise. From these findings, we derive and present implications for tools that can foster inclusion in open source software communities and empower contributions from everyone, everywhere.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2023She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Modelscited by 12
- 2025Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Modelscited by 2
- 2022Intersectionality Goes Analytical: Taming Combinatorial Explosion Through Type Abstractioncited by 0
- 2022A Retrospective on ICSE 2022cited by 0
- 2025Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Models2 Jul 2025
- 2023She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Models17 Mar 2023
- 2022A Retrospective on ICSE 202226 Jul 2022
- 2022Intersectionality Goes Analytical: Taming Combinatorial Explosion Through Type Abstraction25 Jan 2022