To What Extent Does Agent-generated Code Require Maintenance? An Empirical Study
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910204552019968 |
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| author | Sawada, Shota Shirai, Tatsuya Kashiwa, Yutaro Yamaguchi, Ken'ichi Iwata, Hiroshi Iida, Hajimu |
| author_facet | Sawada, Shota Shirai, Tatsuya Kashiwa, Yutaro Yamaguchi, Ken'ichi Iwata, Hiroshi Iida, Hajimu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06464 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | To What Extent Does Agent-generated Code Require Maintenance? An Empirical Study Sawada, Shota Shirai, Tatsuya Kashiwa, Yutaro Yamaguchi, Ken'ichi Iwata, Hiroshi Iida, Hajimu Software Engineering 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. |
| title | To What Extent Does Agent-generated Code Require Maintenance? An Empirical Study |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2605.06464 |