What is it about?

AI agents are increasingly used to help fix software bugs. However, an agent may sound confident even when it does not have enough information to solve the problem reliably. Our work studies how software repair agents judge bug reports, how their judgments change when important information is missing, and whether their behavior during repair reveals signs of uncertainty or ineffective progress. We find that an agent’s confidence alone is not enough to decide whether its repair should be trusted. Instead, we need to consider the available evidence, the agent’s repair behavior, and its own post-repair judgment together. This can help identify cases where the agent’s result needs further validation or human takeover.

Featured Image

Why is it important?

As AI agents become more involved in software engineering, it is important to know not only whether they can generate fixes, but also when their fixes should be trusted. This work is timely because agentic coding tools are rapidly entering real development workflows, where incorrect repairs can waste developer time or introduce new risks. Our study shows that confidence and competence can diverge: agents may report that a task is clear or easy even when key information is missing and repair success drops. By combining issue-report evidence, observable repair behavior, and post-repair self-judgment, this work points toward more reliable software repair agents that can trigger human takeover when autonomous repair is likely to be unreliable or unsuccessful.

Perspectives

For me, this work is about moving beyond the question “Can an AI agent fix this bug?” toward a more practical question: “When should we believe the agent, and when should a human step in?” In real software development, a confident answer is not always a reliable answer. I hope this work encourages researchers and tool builders to design repair agents that expose uncertainty, make their repair process inspectable, and support timely human intervention when key information is missing.

Mingyue Yuan
University of New South Wales

Read the Original

This page is a summary of: Confidence vs. Competence: Misalignment in Judgment and Performance for Agentic Software Repair, ACM Transactions on Software Engineering and Methodology, July 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3822603.
You can read the full text:

Read

Contributors

The following have contributed to this page