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

arXiv: https://arxiv.org/abs/2410.02091 · HTML: https://mlsanity.com/paper/2410.02091/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 71 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot](https://mlsanity.com/paper/2409.08379/) (2024)
- [Predicting the Popularity of GitHub Repositories](https://mlsanity.com/paper/1607.04342/) (2016)
- [A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data](https://mlsanity.com/paper/2107.00856/) (2021)
- [Open Collaboration for Innovation: Principles and Performance](https://mlsanity.com/paper/1406.7541/) (2014)
- [Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Design](https://mlsanity.com/paper/2102.09948/) (2021)

## Built on by (most cited first, 11 of 11)

- [Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects](https://mlsanity.com/paper/2511.04427/) (2025)
- [AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software](https://mlsanity.com/paper/2512.18567/) (2025)
- [Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming](https://mlsanity.com/paper/2511.18849/) (2025)
- [Orchestrating Human-AI Software Delivery: A Retrospective Longitudinal Field Study of Three Software Modernization Programs](https://mlsanity.com/paper/2603.20028/) (2026)
- [Artificial Intelligence in Open Source Software Engineering: A Foundation for Sustainability](https://mlsanity.com/paper/2602.07071/) (2026)
- [Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code](https://mlsanity.com/paper/2605.05267/) (2026)
- [Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review](https://mlsanity.com/paper/2605.17548/) (2026)
- [Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI](https://mlsanity.com/paper/2607.01418/) (2026)
- [RescueLens: LLM-Powered Triage and Action on Volunteer Feedback for Food Rescue](https://mlsanity.com/paper/2511.15698/) (2025)
- [From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software](https://mlsanity.com/paper/2607.05677/) (2026)
- [Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code](https://mlsanity.com/paper/2607.09902/) (2026)

## Abstract (arXiv)

Generative artificial intelligence (AI) facilitates content production and enhances ideation, with potentially important implications for 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 accompanied by a 3.4% increase in developer coding participation and a 2.1% increase in individual code contributions. However, Copilot use is also associated with an 8% increase in coordination time and 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 overall effect remains positive, resulting in a net increase in the timely merge of code contributions at the project level. Interestingly, we also find heterogeneous effects across developer roles. Peripheral developers exhibit relatively smaller increases in project-level code contributions and larger increases in coordination time than core developers. Together, our findings highlight the dual effects of AI pair programmers on code contributions and coordination in OSS development and provide implications for how generative AI may reshape the structure of OSS communities over time.