Hot Fixing in the Wild
2026-04-29 • Software Engineering
Software Engineering
AI summaryⓘ
The authors studied urgent software fixes, called hot fixes, by analyzing over 61,000 GitHub repositories. They found that hot fixes tend to involve fewer people, smaller code changes, and less testing compared to regular bug fixes because they need to be done quickly. They also compared hot fixes made by humans and AI coding agents, discovering over 10 different ways each group repairs code. This work helps us understand how hot fixes are done day-to-day and how humans and AI might work together in the future.
hot fixessoftware maintenanceGitHub repositoriescode reviewautonomous coding agentsbug fixescollaboration in softwareAI-authored codecode changessoftware testing
Authors
Carol Hanna, Karine Even-Mendoza, W. B. Langdon, Mar Zamorano López, Justyna Petke, Federica Sarro
Abstract
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