A new study published on arXiv investigates how code cleanliness impacts the performance of AI coding agents. Researchers created minimal pairs of code repositories that differed only in their structural and stylistic quality, while maintaining identical functionality. The study found that while code cleanliness did not affect the success rate of agents like Claude Code, it significantly reduced their computational cost, using 7-8% fewer tokens and reducing file revisitations by 34%. These findings highlight that traditional software maintainability principles remain crucial for optimizing the efficiency of AI-driven development. AI
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IMPACT Confirms traditional code quality metrics reduce AI agent operational costs, guiding future development practices.
RANK_REASON Academic paper detailing a controlled study on AI agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]