Hot Fixing in the Wild
Carol Hanna, Karine Even-Mendoza, W. B. Langdon, Mar Zamorano López, Justyna Petke + 1 more
TLDR
This study analyzes over 61,000 GitHub hot fixes, revealing their urgent characteristics and distinct repair behaviors between human and AI agents.
Key contributions
- Empirically analyzed over 61,000 GitHub hot fixes, revealing their urgent characteristics like reduced collaboration and minimal testing.
- Identified over 10 distinct repair behaviors by comparing human-authored and AI-agent-authored hot fixes.
- Offers the first large-scale empirical analysis of hot fix code changes, providing insights into human-automation collaboration.
Why it matters
This study offers the first large-scale empirical analysis of hot fixes, revealing critical insights into their urgent nature and operational workflows. Understanding these patterns, especially the differences between human and AI agent behaviors, is crucial for optimizing future human-automation collaboration in software maintenance.
Original 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
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