LintEval: Benchmarking LLMs on Linter-Guided File Refactoring

Abstract:Linters are widely used in code maintenance and real-time IDE editing to surface lint alerts related to performance, style, error-prone, and other patterns in source files. However, existing code benchmarks do not explicitly evaluate linter-guided file refactoring, where models must reduce lint alerts in source files with correct initial functionality while satisfying functionality preservation. We introduce LintEval, a multilingual benchmark for linter-guided file refactoring across C++, Python, and Java, comprising 13,061 programs with correct initial functionality and 171,662 lint alerts extracted using language-specific linters. LintEval jointly evaluates functionality preservation, lint alert reduction, new alert avoidance, and edit minimality. Experiments reveal a persistent trade-off: closed-source commercial models reach up to 90.4% Reduction@P but achieve only 67.1%-68.3% Pass@1, while several open-source small models show low or negative Reduction@P. A controlled fine-tuning study further shows that supervision format matters: refactoring-oriented SFT raises Qwen2.5-Coder-7B Reduction@P from 24.7% to 76.0%. LintEval enables controlled evaluation of models that improve source files under functionality preservation.

Accepted to Findings of EMNLP 2026. Code available at this https URL

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

arXiv:2610.20027 [cs.SE]

(or arXiv:2610.20027v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2610.20027 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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