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    <abstract>Understanding and reasoning about entire soft-ware repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and CodeQA have advanced the field, they predominantly focus on small, self-contained code snippets. These setups fail to capture the complexity of real-world repositories, where effective understanding and reasoning often require navigating multiple files, understanding software architecture, and grounding answers in long-range code dependencies. In this paper, we present SWE-QA, a repository-level code question answering (QA) benchmark designed to facilitate research on automated QA systems in realistic code environments. SWE-QA involves 720 high-quality question-answer pairs spanning diverse categories, including intention understanding, cross-file reasoning, and multi-hop dependency analysis. To construct SWE-QA, we first crawled 77,100 GitHub issues from 12 popular repositories. Based on an analysis of naturally occurring developer questions extracted from these issues, we developed a two-level taxonomy of repository-level questions and constructed a set of seed questions for each category. For each category, we manually curated and validated questions and collected their corresponding answers. We evaluate six advanced LLMs on SWE-QA under various context augmentation strategies. Experimental results highlight the promise of LLMs.</abstract>
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%0 Conference Proceedings
%T SWE-QA: Can Language Models Answer Repository-level Code Questions?
%A Peng, Weihan
%A Shi, Yuling
%A Wang, Yuhang
%A Zhang, Xinyun
%A Shen, Beijun
%A Gu, Xiaodong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F peng-etal-2026-swe
%X Understanding and reasoning about entire soft-ware repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and CodeQA have advanced the field, they predominantly focus on small, self-contained code snippets. These setups fail to capture the complexity of real-world repositories, where effective understanding and reasoning often require navigating multiple files, understanding software architecture, and grounding answers in long-range code dependencies. In this paper, we present SWE-QA, a repository-level code question answering (QA) benchmark designed to facilitate research on automated QA systems in realistic code environments. SWE-QA involves 720 high-quality question-answer pairs spanning diverse categories, including intention understanding, cross-file reasoning, and multi-hop dependency analysis. To construct SWE-QA, we first crawled 77,100 GitHub issues from 12 popular repositories. Based on an analysis of naturally occurring developer questions extracted from these issues, we developed a two-level taxonomy of repository-level questions and constructed a set of seed questions for each category. For each category, we manually curated and validated questions and collected their corresponding answers. We evaluate six advanced LLMs on SWE-QA under various context augmentation strategies. Experimental results highlight the promise of LLMs.
%U https://aclanthology.org/2026.findings-acl.402/
%P 8230-8245

Markdown (Informal)

[SWE-QA: Can Language Models Answer Repository-level Code Questions?](https://aclanthology.org/2026.findings-acl.402/) (Peng et al., Findings 2026)

• SWE-QA: Can Language Models Answer Repository-level Code Questions? (Peng et al., Findings 2026)

ACL

• Weihan Peng, Yuling Shi, Yuhang Wang, Xinyun Zhang, Beijun Shen, and Xiaodong Gu. 2026. SWE-QA: Can Language Models Answer Repository-level Code Questions?. In Findings of the Association for Computational Linguistics: ACL 2026, pages 8230–8245, San Diego, California, United States. Association for Computational Linguistics.