The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot

• URL: http://arxiv.org/abs/2410.02091v2
• Date: Tue, 08 Jul 2025 17:44:42 GMT
• Title: The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot
• Authors: Fangchen Song, Ashish Agarwal, Wen Wen,
• Abstract summary: Using GitHub's proprietary Copilot usage data, we find that Copilot use increases project-level code contributions by 5.9%.<n>This gain is driven by a 2.1% increase in individual code contributions and a 3.4% rise in developer coding participation.<n>While AI expands who can contribute and how much they contribute, it slows coordination in collective development efforts.
• Score: 4.8256226973915455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative artificial intelligence (AI) enables automated content production, including coding in software development, which can significantly influence developer participation and performance. To explore its impact on collaborative open-source software (OSS) development, we investigate the role of GitHub Copilot, a generative AI pair programmer, in OSS development where multiple distributed developers voluntarily collaborate. Using GitHub's proprietary Copilot usage data, combined with public OSS repository data obtained from GitHub, we find that Copilot use increases project-level code contributions by 5.9%. This gain is driven by a 2.1% increase in individual code contributions and a 3.4% rise in developer coding participation. However, these benefits come at a cost as coordination time for code integration increases by 8% due to more code discussions enabled by AI pair programmers. This reveals an important tradeoff: While AI expands who can contribute and how much they contribute, it slows coordination in collective development efforts. Despite this tension, the combined effect of these two competing forces remains positive, indicating a net gain in overall project-level productivity from using AI pair programmers. Interestingly, we also find the effects differ across developer roles. Peripheral developers show relatively smaller gains in project-level code contributions and face a higher increase in coordination time than core developers, likely due to the difference in their project familiarity. In summary, our study underscores the dual role of AI pair programmers in affecting project-level code contributions and coordination time in OSS development. Our findings on the differential effects between core and peripheral developers also provide important implications for the structure of OSS communities in the long run.

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