analiza aiqbench
CodeTaste benchmark reveals that while LLM agents can reliably implement detailed refactoring instructions, they struggle to autonomously identify the specific human-preferred transformations in complex codebases. The study suggests that a propose-then-implement decomposition strategy significantly improves alignment with human developer choices. This work provides a critical evaluation framework for advancing coding agents beyond basic functionality generation toward maintaining architectural quality.
CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
arXiv:2603.04177v2 Announce Type: replace-cross Abstract: LLM coding agents can generate working code, but their solutions often accumulate complexity, duplication, and architectural debt. Human developers address such issues through refactoring: behavior-preserving program transformations that improve structure and maintainability. We investigate whether agents (i) can execute refactorings reliably and (ii) identify the refactorings that human developers actually chose in real codebases. To this end, we construct CodeTaste, a benchmark mined from large multi-file open-source refactorings. To score solutions, we combine repository test suites that measure functional correctness with tailored static checks that verify removal of undesired and introduction of desired code patterns using dataflow reasoning. Our results show a clear gap: agents perform well at implementing refactorings that are specified in detail, but often fail to discover the human refactoring choices when given a focus area for changes. A propose-then-implement decomposition improves alignment, and selecting the best-aligned proposal before implementation can yield further gains. CodeTaste provides an evaluation target and a potential preference signal for aligning coding agents with human refactoring decisions in realistic codebases. We release the benchmark, leaderboard, and code.
Title:CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
Abstract:LLM coding agents can generate working code, but their solutions often accumulate complexity, duplication, and architectural debt. Human developers address such issues through refactoring: behavior-preserving program transformations that improve structure and maintainability. We investigate whether agents (i) can execute refactorings reliably and (ii) identify the refactorings that human developers actually chose in real codebases. To this end, we construct CodeTaste, a benchmark mined from large multi-file open-source refactorings. To score solutions, we combine repository test suites that measure functional correctness with tailored static checks that verify removal of undesired and introduction of desired code patterns using dataflow reasoning. Our results show a clear gap: agents perform well at implementing refactorings that are specified in detail, but often fail to discover the human refactoring choices when given a focus area for changes. A propose-then-implement decomposition improves alignment, and selecting the best-aligned proposal before implementation can yield further gains. CodeTaste provides an evaluation target and a potential preference signal for aligning coding agents with human refactoring decisions in realistic codebases. We release the benchmark, leaderboard, and code.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2603.04177 [cs.SE]
(or arXiv:2603.04177v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2603.04177 Focus to learn more arXiv-issued DOI via DataCite
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