Paper Detail

Hot Fixing in the Wild

Carol Hanna, Karine Even-Mendoza, W. B. Langdon, Mar Zamorano López, Justyna Petke, Federica Sarro

arxiv Score 5.2

Published 2026-04-29 · First seen 2026-04-30

General AI

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

Workflow Status

Review status
pending
Role
unreviewed
Read priority
later
Vote
Not set.
Saved
no
Collections
Not filed yet.
Next action
Not filled yet.

Reading Brief

No structured notes yet. Add `summary_sections`, `why_relevant`, `claim_impact`, or `next_action` in `papers.jsonl` to enrich this view.

Why It Surfaced

No ranking explanation is available yet.

Tags

No tags.

BibTeX

@article{hanna2026hot,
  title = {Hot Fixing in the Wild},
  author = {Carol Hanna and Karine Even-Mendoza and W. B. Langdon and Mar Zamorano López and Justyna Petke and Federica Sarro},
  year = {2026},
  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 f},
  url = {https://arxiv.org/abs/2604.26892},
  keywords = {cs.SE},
  eprint = {2604.26892},
  archiveprefix = {arXiv},
}

Metadata

{}