What Makes a Good Bug Report for an AI Agent?

2026-06-14 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

Research investigating optimal bug report features for AI repair agents reveals that qualities beneficial for human developers do not entirely transfer. Two studies, one statistical on 433 SWE-bench Verified issues with 87 agents and another controlled ablation on SWE-bench Pro with Qwen3.6-35B-A3B and Gemma-4-31B-IT models, identified key differences. Findings indicate that fix suggestions (OR = 3.61), reproduction scripts (OR = 2.52), repository source code (OR = 2.82), and explicit file localization (OR = 2.33) significantly increase resolution likelihood for agents. Conversely, longer reports (OR = 0.49) correlate with lower success. While readability (smog scores, OR = 1.55) showed a slight positive association, structural elements like section headers and list formatting also critically impact agent solve rates, even without content changes. The studies conclude that agents benefit most from concrete, executable, and well-localized information, often differing from traditional human-centric report priorities.

Key takeaway

For AI Engineers or ML Engineers developing or deploying automated program repair agents, optimize your bug reports for agent consumption. You should prioritize concrete, executable information like reproduction scripts, repository source code, and explicit fault localization cues. Avoid overly verbose reports, as length correlates negatively with resolution. Ensure clear structural formatting, including section headers and lists, as these independently affect agent solve rates. This approach will significantly improve agent performance.

Key insights

AI agents thrive on concrete, executable, and localized bug report information, differing from human-centric report priorities.

Principles

• Executable evidence boosts agent repair success.
• Localization cues reduce agent search effort.
• Report structure impacts agent solve rates.

Method

The research used statistical modeling on 87 agents across 433 real issues and controlled ablations on 2 models with 17 problem-statement mutations.

In practice

• Prioritize reproduction scripts for agents.
• Include explicit fault localization data.
• Maintain clear report formatting.

Topics

• AI Agents
• Automated Program Repair
• Bug Reports
• LLM Performance
• SWE-bench
• Fault Localization

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.