野火修复
Hot Fixing in the Wild
AI总结：
研究分析了61000余个仓库中的人工和AI代理生成的热修复，发现其具有紧迫性特征，揭示了人类与AI在修复行为上的差异，为实际应用中的热修复协作提供基础。
摘要：
尽管热修复在操作上很重要，但关于其如何重塑常规维护工作流程，特别是在自主编码代理时代的大规模证据仍有限。我们分析了Hao-Li/AIDev数据集中超过61,000个GitHub仓库中的热修复，并发现一致的紧迫性模式：减少协作（通常由单个贡献者完成）、较小且更针对性的变化（中位数2-3个提交和文件，小于10行修改）、有限的审查（通常少于两个审查者）以及比常规bug修复显著更少的测试文件修改，这与它们的紧迫性驱动特性一致。利用相同的紧迫性上下文，我们检查了人类和AI代理生成的热修复之间的差异，揭示了超过10种不同的修复行为，从而为未来的人机协作提供了见解。我们的研究是首次使用仓库级的操作化紧迫性来大规模分析热修复代码更改。人类和代理行为的比较揭示了它们的各自特征，为实际应用中的热修复提供了基础。
英文摘要：
Despite the operational importance of hot fixes, large-scale evidence on how they reshape routine maintenance workflows, particularly in the era of autonomous coding agents, remains limited. We analyse hot fixes present in over 61,000 GitHub repositories from the Hao-Li/AIDev dataset and find consistent patterns of urgency: reduced collaboration (typically a single contributor), smaller and more targeted changes (median 2-3 commits and files, with <10 line modifications), limited review (often fewer than two reviewers), and substantially fewer test file modifications than regular bug fixes, consistent with their urgency-driven character. Leveraging the same urgency contexts, we examine differences between human- and AI-agent-authored hot fixes, revealing over 10 distinct repair behaviours, thus offering insights into future human-automation collaboration for hot fixing. Our study is the first to empirically analyse hot fix code changes at scale using a repository-level operationalisation of urgency. The comparison of human and agentbehaviours delineates their distinct characteristics, providing a foundation for understanding hot fixing in real-world practice
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。
