When Models Edit Too Much: On the Fidelity of Minimal Code Edits
Large language models (LLMs) are increasingly used to edit existing code, but correctness alone is not enough: useful repairs should also be minimal, reviewable, and faithful to the original implementation. We study over-editing, the tendency of a model to rewrite code beyond what is required to fix a bug. We construct an evaluation framework from 400 BigCodeBench problems by injecting controlled AST-level corruptions into reference solutions, giving each repair task a known minimal patch. Across frontier LLMs, over-editing is widespread even among strong models like GPT-5.5: high Pass@1 can coexist with unnecessarily large edits and added cognitive complexity. A preservation instruction ...
| Authors | Tongyao Zhu, Wei Hern Lim, Min-Yen Kan |
|---|---|
| Primary category | cs.SE |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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