@inproceedings{patel-etal-2026-agent,
title = "Is Agent Code Less Maintainable Than Human Code?",
author = "Patel, Shaswat P and
Hou, Betty Li and
Purohit, Arun and
Xu, Kai and
Pan, Jane and
He, He and
Chen, Valerie",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2528/",
pages = "57491--57514",
ISBN = "979-8-89176-459-0",
abstract = "Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding agents have demonstrated strong performance on single-issue tasks, it remains unclear how maintainable their code is when future agents build on top of it, potentially leading to compounding downstream effects. We investigate how agent code compares to human code in these maintenance settings, presenting $\textbf{CodeThread}$, a framework to construct controlled experiments from repository-level coding benchmarks. Applying CodeThread to four frontier coding agents and four benchmarks, we find that agents are less effective at resolving tasks when building on agent code compared to human code, with task resolve rate drops of up to 13.1{\%}. Regression analysis reveals that many traditional software engineering maintainability metrics do not explain this difference. Instead, the clearest signals are subtler behavioral differences in agent code, such as changes to input validation and error handling, along with differences in downstream code size and task difficulty. These findings highlight the need to evaluate coding agents not only by immediate task resolution but also by maintainability, and point to potential sources of downstream errors introduced by agent code."
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<abstract>Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding agents have demonstrated strong performance on single-issue tasks, it remains unclear how maintainable their code is when future agents build on top of it, potentially leading to compounding downstream effects. We investigate how agent code compares to human code in these maintenance settings, presenting CodeThread, a framework to construct controlled experiments from repository-level coding benchmarks. Applying CodeThread to four frontier coding agents and four benchmarks, we find that agents are less effective at resolving tasks when building on agent code compared to human code, with task resolve rate drops of up to 13.1%. Regression analysis reveals that many traditional software engineering maintainability metrics do not explain this difference. Instead, the clearest signals are subtler behavioral differences in agent code, such as changes to input validation and error handling, along with differences in downstream code size and task difficulty. These findings highlight the need to evaluate coding agents not only by immediate task resolution but also by maintainability, and point to potential sources of downstream errors introduced by agent code.</abstract>
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%0 Conference Proceedings
%T Is Agent Code Less Maintainable Than Human Code?
%A Patel, Shaswat P.
%A Hou, Betty Li
%A Purohit, Arun
%A Xu, Kai
%A Pan, Jane
%A He, He
%A Chen, Valerie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F patel-etal-2026-agent
%X Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding agents have demonstrated strong performance on single-issue tasks, it remains unclear how maintainable their code is when future agents build on top of it, potentially leading to compounding downstream effects. We investigate how agent code compares to human code in these maintenance settings, presenting CodeThread, a framework to construct controlled experiments from repository-level coding benchmarks. Applying CodeThread to four frontier coding agents and four benchmarks, we find that agents are less effective at resolving tasks when building on agent code compared to human code, with task resolve rate drops of up to 13.1%. Regression analysis reveals that many traditional software engineering maintainability metrics do not explain this difference. Instead, the clearest signals are subtler behavioral differences in agent code, such as changes to input validation and error handling, along with differences in downstream code size and task difficulty. These findings highlight the need to evaluate coding agents not only by immediate task resolution but also by maintainability, and point to potential sources of downstream errors introduced by agent code.
%U https://aclanthology.org/2026.emnlp-main.2528/
%P 57491-57514
Markdown (Informal)
[Is Agent Code Less Maintainable Than Human Code?](https://aclanthology.org/2026.emnlp-main.2528/) (Patel et al., EMNLP 2026)
ACL
- Shaswat P Patel, Betty Li Hou, Arun Purohit, Kai Xu, Jane Pan, He He, and Valerie Chen. 2026. Is Agent Code Less Maintainable Than Human Code?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57491–57514, Budapest, Hungary. Association for Computational Linguistics.