@inproceedings{jin-etal-2026-chainswe,
title = "{C}hain{SWE}: Benchmarking Coding Agents on Multi-Bug Software Maintenance",
author = "Jin, Qirui and
Tung, Lingching and
Li, Kenan and
Shi, Qiyang and
She, Yushi and
Jia, Huanzhong and
Zhao, Harrison and
Xia, Kejing and
Du, Zhenbang and
Pei, Jiaxin and
Zhang, Zhenyu and
Qi, Zhen and
Duan, Yuyan and
Lee, Wenke and
Jin, Zijian",
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.371/",
pages = "8487--8505",
ISBN = "979-8-89176-459-0",
abstract = "Language model (LM) agents are increasingly deployed to maintain codebases over extended periods, fixing streams of related defects while carrying context from one fix to the next. Yet existing software engineering (SWE) benchmarks evaluate models one bug at a time: the repository is reset, the codebase is re-read, and a single self-contained issue is graded in isolation. This setting collapses a continuous maintenance workflow into a series of independent sessions, ignoring the cumulative dependencies that make real-world bug fixing challenging. To bridge this gap, we introduce ChainSWE, the first benchmark for evaluating agents on sequential, dependent bug fixes within a shared codebase. We collect chronological chains of 304 issues across 54 Python projects, mined from six SWE-bench-family datasets. Our evaluation across a range of agents and models reveals a consistent performance drop by **up to 70{\%}** as the chain length increases. Our data and code is available at [C-lister/ChainSWE](https://huggingface.co/datasets/C-lister/ChainSWE)."
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<abstract>Language model (LM) agents are increasingly deployed to maintain codebases over extended periods, fixing streams of related defects while carrying context from one fix to the next. Yet existing software engineering (SWE) benchmarks evaluate models one bug at a time: the repository is reset, the codebase is re-read, and a single self-contained issue is graded in isolation. This setting collapses a continuous maintenance workflow into a series of independent sessions, ignoring the cumulative dependencies that make real-world bug fixing challenging. To bridge this gap, we introduce ChainSWE, the first benchmark for evaluating agents on sequential, dependent bug fixes within a shared codebase. We collect chronological chains of 304 issues across 54 Python projects, mined from six SWE-bench-family datasets. Our evaluation across a range of agents and models reveals a consistent performance drop by **up to 70%** as the chain length increases. Our data and code is available at [C-lister/ChainSWE](https://huggingface.co/datasets/C-lister/ChainSWE).</abstract>
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%0 Conference Proceedings
%T ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance
%A Jin, Qirui
%A Tung, Lingching
%A Li, Kenan
%A Shi, Qiyang
%A She, Yushi
%A Jia, Huanzhong
%A Zhao, Harrison
%A Xia, Kejing
%A Du, Zhenbang
%A Pei, Jiaxin
%A Zhang, Zhenyu
%A Qi, Zhen
%A Duan, Yuyan
%A Lee, Wenke
%A Jin, Zijian
%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 jin-etal-2026-chainswe
%X Language model (LM) agents are increasingly deployed to maintain codebases over extended periods, fixing streams of related defects while carrying context from one fix to the next. Yet existing software engineering (SWE) benchmarks evaluate models one bug at a time: the repository is reset, the codebase is re-read, and a single self-contained issue is graded in isolation. This setting collapses a continuous maintenance workflow into a series of independent sessions, ignoring the cumulative dependencies that make real-world bug fixing challenging. To bridge this gap, we introduce ChainSWE, the first benchmark for evaluating agents on sequential, dependent bug fixes within a shared codebase. We collect chronological chains of 304 issues across 54 Python projects, mined from six SWE-bench-family datasets. Our evaluation across a range of agents and models reveals a consistent performance drop by **up to 70%** as the chain length increases. Our data and code is available at [C-lister/ChainSWE](https://huggingface.co/datasets/C-lister/ChainSWE).
%U https://aclanthology.org/2026.emnlp-main.371/
%P 8487-8505
Markdown (Informal)
[ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance](https://aclanthology.org/2026.emnlp-main.371/) (Jin et al., EMNLP 2026)
ACL
- Qirui Jin, Lingching Tung, Kenan Li, Qiyang Shi, Yushi She, Huanzhong Jia, Harrison Zhao, Kejing Xia, Zhenbang Du, Jiaxin Pei, Zhenyu Zhang, Zhen Qi, Yuyan Duan, Wenke Lee, and Zijian Jin. 2026. ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8487–8505, Budapest, Hungary. Association for Computational Linguistics.