Precise Debugging Benchmark: Is Your Model Debugging or Regenerating?

Title:Precise Debugging Benchmark: Is Your Model Debugging or Regenerating?

Abstract:Unlike code completion, debugging requires localizing faults and applying targeted edits. We observe that frontier LLMs often regenerate correct but over-edited solutions during debugging. To evaluate how far LLMs are from precise debugging, we introduce the Precise Debugging Benchmark (PDB) framework, which automatically converts any coding dataset into a debugging benchmark with precision-aware evaluation. PDB generates buggy programs by synthesizing verified atomic bugs and composing them into multi-bug programs. We define two novel metrics, edit-level precision and bug-level recall, which measure how many necessary edits are made and how many bugs are resolved. We release two evaluation benchmarks: PDB-Single-Hard on single-line bugs, and PDB-Multi on multi-line bugs. Experiments show that frontier models, such as GPT-5.1-Codex and DeepSeek-V3.2-Thinking, achieve unit-test pass rates above 76% but exhibit precision below 45%, even when explicitly instructed to perform minimal debugging. Finally, we show that iterative and agentic debugging strategies do not substantially improve precision or recall, highlighting the need to rethink post-training pipelines for coding models.

Software Engineering (cs.SE); Computation and Language (cs.CL)

arXiv:2604.17338 [cs.SE]

(or arXiv:2604.17338v5 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2604.17338 Focus to learn more arXiv-issued DOI via DataCite

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