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

翻译：尽管热修复在运维中至关重要，但关于其如何重塑常规维护工作流程（尤其是在自主编码代理时代）的大规模证据仍然有限。我们分析了来自Hao-Li/AIDev数据集中超过61,000个GitHub仓库的热修复，并发现了紧迫性的一致模式：协作减少（通常为单一贡献者）、变更范围更小且更具针对性（中位数为2-3次提交和文件，修改行数<10行）、审查有限（通常少于两位审查者），以及测试文件修改量显著少于常规错误修复，这与其紧迫性驱动的特性一致。利用相同的紧迫性上下文，我们考察了人类与AI代理编写的热修复之间的差异，揭示了超过10种不同的修复行为，从而为未来热修复中的人机协作提供了见解。本研究首次通过仓库级别的紧迫性操作化对大规模热修复代码变更进行了实证分析。人类与代理行为的比较勾勒出它们各自的特性，为理解实际实践中的热修复奠定了基础。