@inproceedings{zhang-etal-2026-swe,
title = "{SWE}-Edit: Rethinking Code Editing for Efficient {SWE}-Agent",
author = "Zhang, Yikai and
Pei, Jiaxin and
Li, Kenan and
Jin, Qirui and
Wang, Maoquan and
Pan, Jin and
Kang, Yu and
Fu, Shengyu and
Nallipogu, Elsie and
Hu, Junjie and
Huang, Yufan and
Jin, Zijian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.485/",
pages = "10168--10185",
ISBN = "979-8-89176-462-0",
abstract = "Large language model agents have made strong progress on software engineering, yet current systems suffer from a context coupling problem: the standard code editing interface conflates code inspection, modification planning, and edit execution within a single context window, forcing agents to interleave exploratory viewing with strictly formatted edit generation. Irrelevant context accumulates and edit reliability degrades. We propose SWE-Edit, which decomposes the editing interface into two specialized subagents: a Viewer that extracts task-relevant code on demand, and an Editor that executes modifications from high-level natural language plans{---}letting the main agent focus on reasoning while delegating context-intensive operations to clean context windows. We further show that effective edit-format selection can be trained into a small model rather than requiring frontier-scale capacity: GRPO training on Qwen3-8B improves edit success by 12.5 pp and brings an 8B open-source editor to parity with GPT-5-nano on downstream SWE-Bench resolve rate, with the learned adaptive find-replace/whole-file-rewrite policy contributing primarily on the cost axis ($-11.8\%$ agent cost). To enable rapid editor iteration, we release PR-Edit, a lightweight editor-level evaluation that rank-orders the editor models we test consistently with their downstream SWE-Bench resolve rate. We release our code at \url{https://github.com/microsoft/SWE-Edit}."
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<abstract>Large language model agents have made strong progress on software engineering, yet current systems suffer from a context coupling problem: the standard code editing interface conflates code inspection, modification planning, and edit execution within a single context window, forcing agents to interleave exploratory viewing with strictly formatted edit generation. Irrelevant context accumulates and edit reliability degrades. We propose SWE-Edit, which decomposes the editing interface into two specialized subagents: a Viewer that extracts task-relevant code on demand, and an Editor that executes modifications from high-level natural language plans—letting the main agent focus on reasoning while delegating context-intensive operations to clean context windows. We further show that effective edit-format selection can be trained into a small model rather than requiring frontier-scale capacity: GRPO training on Qwen3-8B improves edit success by 12.5 pp and brings an 8B open-source editor to parity with GPT-5-nano on downstream SWE-Bench resolve rate, with the learned adaptive find-replace/whole-file-rewrite policy contributing primarily on the cost axis (-11.8% agent cost). To enable rapid editor iteration, we release PR-Edit, a lightweight editor-level evaluation that rank-orders the editor models we test consistently with their downstream SWE-Bench resolve rate. We release our code at https://github.com/microsoft/SWE-Edit.</abstract>
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%0 Conference Proceedings
%T SWE-Edit: Rethinking Code Editing for Efficient SWE-Agent
%A Zhang, Yikai
%A Pei, Jiaxin
%A Li, Kenan
%A Jin, Qirui
%A Wang, Maoquan
%A Pan, Jin
%A Kang, Yu
%A Fu, Shengyu
%A Nallipogu, Elsie
%A Hu, Junjie
%A Huang, Yufan
%A Jin, Zijian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-swe
%X Large language model agents have made strong progress on software engineering, yet current systems suffer from a context coupling problem: the standard code editing interface conflates code inspection, modification planning, and edit execution within a single context window, forcing agents to interleave exploratory viewing with strictly formatted edit generation. Irrelevant context accumulates and edit reliability degrades. We propose SWE-Edit, which decomposes the editing interface into two specialized subagents: a Viewer that extracts task-relevant code on demand, and an Editor that executes modifications from high-level natural language plans—letting the main agent focus on reasoning while delegating context-intensive operations to clean context windows. We further show that effective edit-format selection can be trained into a small model rather than requiring frontier-scale capacity: GRPO training on Qwen3-8B improves edit success by 12.5 pp and brings an 8B open-source editor to parity with GPT-5-nano on downstream SWE-Bench resolve rate, with the learned adaptive find-replace/whole-file-rewrite policy contributing primarily on the cost axis (-11.8% agent cost). To enable rapid editor iteration, we release PR-Edit, a lightweight editor-level evaluation that rank-orders the editor models we test consistently with their downstream SWE-Bench resolve rate. We release our code at https://github.com/microsoft/SWE-Edit.
%U https://aclanthology.org/2026.findings-emnlp.485/
%P 10168-10185
Markdown (Informal)
[SWE-Edit: Rethinking Code Editing for Efficient SWE-Agent](https://aclanthology.org/2026.findings-emnlp.485/) (Zhang et al., Findings 2026)
ACL
- Yikai Zhang, Jiaxin Pei, Kenan Li, Qirui Jin, Maoquan Wang, Jin Pan, Yu Kang, Shengyu Fu, Elsie Nallipogu, Junjie Hu, Yufan Huang, and Zijian Jin. 2026. SWE-Edit: Rethinking Code Editing for Efficient SWE-Agent. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 10168–10185, Budapest, Hungary. Association for Computational Linguistics.