从对话到贡献：开源软件中编码代理的特征分析
From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software
AI总结：
研究开源软件中开发者与人工智能编码代理聊天交互和后续开发协作的关系，通过收集大量会话及关联仓库历史等进行分析，发现小仓库AI使用多，采用后贡献者有变化，还揭示了开发者对AI代码的看法及相关担忧，为开源开发实践提供见解。
摘要：
诸如GitHub Copilot和Cursor等人工智能编码助手已从代码建议工具演变为对话式协作工具，实现了氛围编码工作流程，开发者可通过自然语言对话引导人工智能生成的代码。尽管研究人员日益认识到人工智能编码代理的重要性并开始研究其对开源开发的影响，但对开发者与人工智能基于聊天的交互如何与后续开源开发和协作相关的全面理解仍有限。为填补这一空白，我们收集了13,360个人工智能对话会话，包含来自1,356个开源软件仓库的79,172条用户消息，并将它们与仓库开发历史相关联，还通过有针对性的开发者调查对分析进行补充。我们发现较小、不太成熟且协作性较低的仓库中人工智能使用更频繁。采用人工智能后，项目往往有更多活跃贡献者且贡献者集中度降低（p <.001），尽管沟通仍高度集中。代码编写是主要聊天目的，几乎所有人工智能聊天会话后都有后续提交。我们发现代码质量信号或拉取请求合并率没有普遍下降。然而，开发者认为其他人人工智能生成的代码比自己的更难维护（p =.029），并认为人工智能降低了开源贡献的障碍。虽然大多数开发者（68%）愿意分享他们的聊天记录，但仍担心显得不称职、增加审查负担以及将想法暴露给竞争对手。这些发现提供了人工智能辅助开源贡献的大规模实证特征，并为设计和管理开源开发中负责任的氛围编码实践提供了实用见解。
英文摘要：
AI coding assistants such as GitHub Copilot and Cursor have evolved from code-suggestion tools into conversational collaborators, enabling vibe-coding workflows in which developers guide AI-generated code through natural-language dialogue. Although researchers have increasingly recognized the importance of AI coding agents and begun examining their impact on open-source development, a comprehensive understanding of how developers' chat-based interactions with AI relate to subsequent open-source development and collaboration remains limited. This hinders efforts to effectively design, evaluate, and govern AI-assisted open-source software development. To address this gap, we collected 13,360 AI conversation sessions comprising 79,172 user messages from 1,356 OSS repositories, linked them to repository development histories, and complemented this analysis with a targeted developer survey. We find heavier AI use in smaller, less mature, and less collaborative repositories. After AI adoption, projects tended to show more active contributors and lower contributor concentration (p < .001), although communication remained highly concentrated. Code Writing was the dominant chat purpose, and nearly all AI chat sessions were followed by subsequent commits. We find no broad deterioration in code-quality signals or pull request merging rates. However, developers perceive others' AI-generated code as harder to maintain than their own (p = .029) and view AI as lowering barriers to OSS contribution. While most developers (68%) are willing to share their chat, concerns remain around appearing incompetent, increasing reviewer burden, and exposing ideas to competitors. These findings provide a large-scale empirical characterization of AI-assisted OSS contribution and offer practical insights for designing and governing responsible vibe-coding practices in open-source development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
