Hot Fixing in the Wild

2026-04-29

AI Summary

This study addresses the lack of large-scale empirical analysis on the impact of hotfixes in real-world development contexts, particularly amid the rise of autonomous coding agents. It introduces, for the first time, a repository-level operational definition of urgency and conducts a systematic investigation of hotfix practices across more than 61,000 GitHub repositories by integrating code change mining with comparative analyses of human and AI-driven behaviors. The findings reveal that hotfixes are typically characterized by single-author contributions, minimal code modifications, low engagement in code review, and rarely involve test changes. Moreover, the study uncovers over ten statistically significant behavioral differences between human developers and AI agents in hotfix scenarios, offering crucial empirical insights for future human-AI collaborative software maintenance.

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

Problem

• hot fixes
• software maintenance
• autonomous coding agents
• urgency
• human-automation collaboration

Innovation

• hot fixes
• AI coding agents
• empirical study
• urgency-driven repair
• human-AI collaboration