arXiv:2607.14037cs.SEcs.AI2026-07中稿 · KDD

分析2500多个开源项目如何使用智能编程工具,发现多数项目使用极少。

Early Adoption of Agentic Coding Tools by GitHub Projects

论文配图:Early Adoption of Agentic Coding Tools by GitHub Projects
图 1 · 摘自论文原文
  • 统计2361个GitHub项目在三个月内生成的25264个智能代码提交
  • 超半数项目每三月仅提交1-2次智能代码,小团队参与度更高
  • 多数项目依赖单人审核,协同模式仍不普遍

智能编程工具正日益具备自动生成并提交拉取请求(PR)的能力,为软件开发引入新型人机协作模式。本文分析了25,264个来自2,361个流行GitHub仓库的智能型PR,探究(1)智能工具的采纳情况,(2)项目级智能PR产出效率,以及(3)人机协作模式。结果表明,中位数仓库在三个月内仅生成1至2个智能PR,说明高强度采用仍集中在少数项目中。小型项目(1-5名贡献者)的参与率和平均智能PR活动水平高于中大型项目。项目间智能PR产出效率差异显著:虽少数项目超过行业估算的每参与者36个PR的阈值,但多数项目仍低于此水平。此外,人机协作以单一人类监督为主,即一名开发者负责审查或修改代理生成的内容,多人协作模式仍较罕见。这些发现提供了早期实证证据,揭示开源项目如何组织对智能代码工具的监督,并表明成功整合代理贡献不仅取决于工具能力,还依赖于人类与组织流程。由于本研究捕捉的是智能工具采纳的早期快照,未来工作应持续追踪其演进趋势。

原文摘要 · Abstract (English)

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.

智能编程开源协作人机交互

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