Hot Fixing in the Wild

TL;DR

This study analyzes over 61,000 GitHub hot fixes to understand their patterns, urgency, and differences between human and AI contributions, providing insights into real-world maintenance workflows.

Contribution

First large-scale empirical analysis of hot fix code changes, highlighting urgency-driven patterns and differences between human and AI-authored fixes.

Findings

Hot fixes are typically small, targeted, and involve fewer reviewers.

They exhibit urgency with less collaboration and testing.

Distinct behaviors are observed between human and AI hot fixes.

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