Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics
This addresses the need for organizations to manage AI tooling risks in codebases, though it is incremental as it builds on existing human code quality metrics.
The study tackled the problem of ensuring code is compatible with AI coding agents by investigating AI-friendliness through LLM-based refactoring on 5,000 Python files, finding that human-friendly code metrics like CodeHealth are associated with better semantic preservation after AI edits.
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.