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Does code cleanliness affect coding agents? A controlled minimal-pair study

arxiv.org

July 5, 2026

2 min read

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51/100

Summary

A study investigates the impact of code cleanliness on the performance of autonomous coding agents. The research focuses on how the structural and stylistic quality of code influences an agent's ability to navigate and complete coding tasks.

Key Takeaways

Code cleanliness does not affect the pass rate of coding agents but significantly impacts their operational efficiency.
Agents working with cleaner code use 7 to 8% fewer tokens and reduce file revisitations by 34%.
The study introduces an evaluation protocol using minimal pairs to isolate the effect of code cleanliness on coding agents.
Traditional maintainability principles remain relevant in AI-driven development, influencing the computational cost and navigational efficiency of coding agents.

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Community Sentiment

Mixed

Positives

Agents show a notable performance boost in clean codebases, with less need for extensive code reviews—this matters because efficiency directly impacts development timelines.
Using LLMs to refactor code into idiomatic styles is a game changer; it aligns with community standards and can enhance performance significantly.
Asking agents to apply principles like YAGNI can streamline codebases effectively—this highlights the potential for LLMs to improve overall code quality.

Concerns

There's skepticism about whether agents can handle messy codebases effectively; if they can't navigate them like humans do, it raises concerns over their practical utility.
Relying on AI to clean code might lead to a false sense of security; if the AI's 'clean' isn't truly representative of good practices, it undermines the study's validity.
Anecdotal evidence from users lacks scientific rigor and could mislead discussions about the actual capabilities of coding agents.