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    <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.</abstract>
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%0 Conference Proceedings
%T LintEval: Benchmarking LLMs on Linter-Guided File Refactoring
%A Deng, Tongwei
%A Zhu, Jinyu
%A Cao, Yue
%A Wang, Chenxu
%A Zou, Yanzhen
%A Xie, Bing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F deng-etal-2026-linteval
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.157/
%P 3288-3308

Markdown (Informal)

[LintEval: Benchmarking LLMs on Linter-Guided File Refactoring](https://aclanthology.org/2026.findings-emnlp.157/) (Deng et al., Findings 2026)

• LintEval: Benchmarking LLMs on Linter-Guided File Refactoring (Deng et al., Findings 2026)

ACL

• Tongwei Deng, Jinyu Zhu, Yue Cao, Chenxu Wang, Yanzhen Zou, and Bing Xie. 2026. LintEval: Benchmarking LLMs on Linter-Guided File Refactoring. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 3288–3308, Budapest, Hungary. Association for Computational Linguistics.