发表机构
Rochester Institute of Technology（罗切斯特技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究GitHub项目对智能编码工具的早期采用情况，通过分析大量智能PR，探究采用情况、项目生产力及协作模式，发现多数仓库采用少，小项目参与度高，生产力差异大，人机协作以单人监督为主，为相关管理提供实证依据。
AI 中文摘要
智能编码工具在软件开发中引入了新的人机协作形式。此前研究多关注智能生成贡献的拉取请求（PR）层面结果，对项目层面的采用和管理了解较少。本文分析了来自2361个流行GitHub仓库的25264个智能PR，研究智能编码工具的采用情况、项目层面的智能PR生产力及人机协作模式。结果表明，多数仓库三个月内仅生成一到两个智能PR，小项目参与率和活动水平更高，项目层面生产力差异大，人机协作以单人监督为主。这些发现为开源项目围绕智能编码工具组织人工监督提供了早期实证证据，表明智能生成贡献的成功整合不仅取决于智能能力的进步，还取决于管理其使用的人力和组织流程。鉴于本研究是智能采用的早期快照，未来工作应继续跟踪采用模式如何随时间演变。
英文摘要
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.
CommentsAccepted at the KDD 2026 Workshop on Agentic Software Engineering (SE 3.0)
