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

TL;DR

This study examines how GitHub Copilot, a generative AI tool, impacts open-source software development by increasing contributions and participation but also raising coordination time, with effects varying across developer roles.

Contribution

It provides empirical evidence on the effects of AI pair programming on OSS collaboration, highlighting benefits and tradeoffs in contribution levels and coordination.

Findings

Copilot use increases project contributions by 5.9%.

Participation rises by 3.4%, productivity by 2.1%.

Coordination time increases by 8%, especially for peripheral developers.

Abstract

Generative artificial intelligence (AI) facilitates content production and enhances ideation capabilities, which can significantly influence developer productivity and participation in software development. 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 project data obtained from GitHub, we find that Copilot use increases project-level code contributions by 5.9%. This gain is driven by a 3.4% rise in developer coding participation and a 2.1% increase in individual productivity. However, Copilot use also leads to an increase in coordination time by 8% due to more code discussions. 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 timely merge of code contributions from using AI pair programmers. Interestingly, we also find the effects differ across developer roles. Peripheral developers show relatively smaller increases in project-level code contributions and experience larger increases in coordination time than core developers. 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.