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tech 6 July 2026

The Impact of Code Cleanliness on Coding Agents: A Controlled Minimal-Pair Study

Explore how code cleanliness impacts the performance and efficiency of coding agents in a controlled comparative study.

Article inspired by the original source
Does code cleanliness affect coding agents? A controlled minimal-pair study ↗ arxiv.org

Introduction

With the rapid rise of autonomous coding agents, the question of code cleanliness becomes crucial. While task completion rates are often the primary evaluation criterion, a critical question remains unanswered: does the structural and stylistic quality of code, or its "cleanliness," affect an agent's ability to navigate and modify it? This article explores this question through a controlled minimal-pair study, revealing significant impacts on coding agents.

Study Context

The study conducted by Priyansh Trivedi and Olivier Schmitt from SonarSource focuses on minimal-pair code repositories. These repositories share the same architecture, dependencies, and external behavior but differ in terms of static-analysis rule violations and cognitive complexity. In other words, one repository is "clean" while the other is "messy."

The researchers designed agent pipelines capable of either degrading a clean repository or cleaning a messy one. By evaluating 33 tasks across six pairs, they aimed to observe whether code cleanliness affected agent performance.

Study Findings

Across 660 trials conducted with Claude Code, a coding agent, code cleanliness did not change the agents' pass rates. However, it significantly affected their operational footprint. Agents working on cleaner code used 7 to 8% fewer tokens and reduced file revisitations by 34%.

These findings indicate that traditional maintainability principles remain relevant in the AI-driven development era. Code cleanliness affects not only the computational cost but also the navigational efficiency of agents.

Implications for Software Development

For tech decision-makers and developers, these results raise important considerations. While AI can automate and enhance many aspects of development, code quality remains a key factor. Code cleanliness joins model choice, environment, and prompting as factors materially influencing agent behaviors.

Conclusion

In conclusion, code cleanliness is more than just a best practice; it is essential for optimizing the efficiency of coding agents. For tech companies, ensuring clean code can reduce costs and improve AI agent performance.

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code cleanliness coding agents AI development software engineering code quality
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