Human-Aligned Code Readability Assessment with Large Language Models

• URL: http://arxiv.org/abs/2510.16579v1
• Date: Sat, 18 Oct 2025 17:00:52 GMT
• Title: Human-Aligned Code Readability Assessment with Large Language Models
• Authors: Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang, Pawel Borsukiewicz, Xin Zhou, Anil Koyuncu, Jacques Klein, David Lo, Tegawendé F. Bissyandé,
• Abstract summary: We introduce CoReEval, the first large-scale benchmark for evaluating Large Language Models (LLMs)-based code readability assessment.<n>LLMs offer a scalable alternative, but their behavior as readability evaluators remains underexplored.<n>Our findings show that developer-guided prompting grounded in human-defined readability dimensions improves alignment in structured contexts.
• Score: 15.17270025276759
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Code readability is crucial for software comprehension and maintenance, yet difficult to assess at scale. Traditional static metrics often fail to capture the subjective, context-sensitive nature of human judgments. Large Language Models (LLMs) offer a scalable alternative, but their behavior as readability evaluators remains underexplored. We introduce CoReEval, the first large-scale benchmark for evaluating LLM-based code readability assessment, comprising over 1.4 million model-snippet-prompt evaluations across 10 state of the art LLMs. The benchmark spans 3 programming languages (Java, Python, CUDA), 2 code types (functional code and unit tests), 4 prompting strategies (ZSL, FSL, CoT, ToT), 9 decoding settings, and developer-guided prompts tailored to junior and senior personas. We compare LLM outputs against human annotations and a validated static model, analyzing numerical alignment (MAE, Pearson's, Spearman's) and justification quality (sentiment, aspect coverage, semantic clustering). Our findings show that developer-guided prompting grounded in human-defined readability dimensions improves alignment in structured contexts, enhances explanation quality, and enables lightweight personalization through persona framing. However, increased score variability highlights trade-offs between alignment, stability, and interpretability. CoReEval provides a robust foundation for prompt engineering, model alignment studies, and human in the loop evaluation, with applications in education, onboarding, and CI/CD pipelines where LLMs can serve as explainable, adaptable reviewers.

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