好代码不仅方便人读，也更利于AI修改。
Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics
- 用5000个竞赛代码文件测试AI重构效果
- 发现代码健康度高则AI修改后语义保持率更高
- 适合关注人机协同开发的团队参考
我们正进入人类开发者与AI编程代理共同维护代码库的混合时代。尽管行业长期优化代码以提升人类可读性，但确保不同能力的大型语言模型能可靠地编辑代码变得愈发重要。本研究基于5000个来自编程竞赛的Python文件，通过LLM驱动的重构任务，探究了「AI友好型代码」的概念。结果表明，专为人类理解设计的代码健康度（CodeHealth）指标与AI重构后的语义保留率之间存在显著关联。研究证实，对人类友好的代码同样更适配AI工具。这说明组织可利用CodeHealth识别低风险的AI干预区域，并在高风险处加强人工监督。投资代码可维护性，既服务人类，也为大规模AI应用铺路。
原文摘要 · Abstract (English)
We are entering a hybrid era in which human developers and AI coding agents work in the same codebases. While industry practice has long optimized code for human comprehension, it is increasingly important to ensure that LLMs with different capabilities can edit code reliably. In this study, we investigate the concept of ``AI-friendly code'' via LLM-based refactoring on a dataset of 5,000 Python files from competitive programming. We find a meaningful association between CodeHealth, a quality metric calibrated for human comprehension, and semantic preservation after AI refactoring. Our findings confirm that human-friendly code is also more compatible with AI tooling. These results suggest that organizations can use CodeHealth to guide where AI interventions are lower risk and where additional human oversight is warranted. Investing in maintainability not only helps humans; it also prepares for large-scale AI adoption.
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