Confidence vs. Competence: Misalignment in Judgment and Performance for Agentic Software Repair

Abstract

While Large Language Model (LLM) agents are increasingly applied to automated software repair, misalignment remains in how humans and agents judge issue conditions and how agents’ pre-execution self-assessments relate to repair competence. Human developers rely on diagnostic cues such as reproduction steps and stack traces to judge whether an issue is sufficiently specified, whereas LLM agents often fail to recognize missing information. We present the first systematic empirical study of misalignment between human and agent judgments and between agent judgments and repair performance. Using SWE-bench, controlled ablation experiments establish a causal link between removing human-valued cues and reduced LLM repair success. Specifically, LLM judges show limited agreement with human problem-specification ratings, and agents’ pre-execution self-assessments only weakly track repair degradation when key cues are removed. Our trajectory analysis further reveals distinct behavioral responses to missing information, while post-execution self-judgment signals add useful discriminative information when combined with behavioral traces. These findings reveal a persistent gap between how current repair agents assess issue conditions and how they perform during repair, highlighting the need for judgment-aware and correction-aware agent design.