關於代理編碼的使用：GitHub上拉取請求的實證研究

摘要

大型語言模型（LLMs）正日益被整合到軟體開發流程中。透過使用自主AI代理，能夠以最少的人為干預生成程式碼並提交拉取請求（pull requests），這有望成為標準實踐。然而，對於這些拉取請求的實際效用及其在現實專案中的接受程度，我們知之甚少。本文中，我們實證研究了使用Claude Code（一種代理式編碼工具）生成的567個GitHub拉取請求（PRs），這些請求分佈於157個不同的開源專案中。我們的分析顯示，開發者傾向於依賴代理來處理重構、文件編寫和測試等任務。結果表明，83.8%的這些代理協助的PRs最終被專案維護者接受並合併，其中54.9%的合併PRs在未經進一步修改的情況下被整合。其餘45.1%則需要額外的修改，特別是在錯誤修復、文件編寫以及遵循專案特定標準方面，這些修改受益於人工審閱。這些發現表明，雖然代理協助的PRs在很大程度上是可接受的，但它們仍能從人工監督和精煉中獲益。

English

Large language models (LLMs) are increasingly being integrated into software development processes. The ability to generate code and submit pull requests with minimal human intervention, through the use of autonomous AI agents, is poised to become a standard practice. However, little is known about the practical usefulness of these pull requests and the extent to which their contributions are accepted in real-world projects. In this paper, we empirically study 567 GitHub pull requests (PRs) generated using Claude Code, an agentic coding tool, across 157 diverse open-source projects. Our analysis reveals that developers tend to rely on agents for tasks such as refactoring, documentation, and testing. The results indicate that 83.8% of these agent-assisted PRs are eventually accepted and merged by project maintainers, with 54.9% of the merged PRs are integrated without further modification. The remaining 45.1% require additional changes benefit from human revisions, especially for bug fixes, documentation, and adherence to project-specific standards. These findings suggest that while agent-assisted PRs are largely acceptable, they still benefit from human oversight and refinement.

關於代理編碼的使用：GitHub上拉取請求的實證研究

On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub

摘要

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