LLM-based autonomous coding agents have reshaped software development. While these agents excel at code generation, open questions persist about the long-term maintainability of AI-generated code. This study empirically investigates the maintenance extent, human involvement, and modification types of AI-generated files versus human-authored code. Using the AIDev dataset of AI-generated pull requests and GitHub, we analyzed over 1,000 files and approximately 3,200 changes from 100 popular repositories. Our findings show that: (i) AI-generated files receive less frequent maintenance than human-authored code, with updates affecting only a small fraction of file size; (ii) the most frequent modifications to AI code are feature extensions, whereas human updates focus on bug fixes, and (iii) human developers perform the large majority of this maintenance.
@article{arxiv.2605.06464,
title = {To What Extent Does Agent-generated Code Require Maintenance? An Empirical Study},
author = {Shota Sawada and Tatsuya Shirai and Yutaro Kashiwa and Ken'ichi Yamaguchi and Hiroshi Iwata and Hajimu Iida},
journal= {arXiv preprint arXiv:2605.06464},
year = {2026}
}
Comments
6 pages, 2 figures, 2 tables. Accepted at the 30th International Conference on Evaluation and Assessment in Software Engineering (EASE 2026)