这篇论文用两千多个仓库的真实数据告诉你，小项目反而更爱用AI写代码，但整体上大家用得还不多。想了解实际项目怎么管AI贡献的可以看看。
本研究分析了2361个热门GitHub仓库中的25264个代理PR。结果显示，中位仓库在三个月内仅产生1-2个代理PR，密集采用集中在少数项目。小项目（1-5名贡献者）的代理PR参与比例高于中型和大型项目。人机协作以单人监督为主，即一名开发者审查或修改代理贡献。这些发现提供了代理编码工具在开源项目中采用的早期实证。
Early Adoption of Agentic Coding Tools by GitHub Projects
Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding tools are adopted and managed at the project level. In this paper, we analyze 25,264 agentic PRs from 2,361 popular GitHub repositories to investigate (1) the adoption of agentic coding tools, (2) project-level agentic PR productivity, and (3) human-agent collaboration patterns. Our results show that the median repository generates only one to two agentic PRs during a three-month period, indicating that intensive adoption remains concentrated in a small subset of projects. At the same time, small projects (1-5 contributors) exhibit higher participation ratios and average levels of agentic PR activity than medium-sized and large projects. We also observe substantial variation in project-level agentic PR productivity. While a small number of projects exceed an industry-reported estimate of 36 PRs per participant during the three-month observation period, most projects remain below this threshold. Finally, human-agent collaboration is dominated by a single-human oversight model, in which one developer reviews and/or modifies the agent's contributions, while multi-human collaboration patterns remain uncommon. These findings provide early empirical evidence on how open-source projects organize human oversight around agentic coding tools and suggest that successful integration of agent-generated contributions depends not only on advances in agent capabilities but also on the human and organizational processes that govern their use. Because this study captures an early snapshot of agent adoption, future work should continue to track how adoption patterns evolve over time.