title: '[2310.06770] SWE-bench: Can Language Models Resolve Real-World GitHub Issues?'
id: 231006770-swe-bench-can-language-models-resolve-real-world-github-issues
tags:
- deepread
created: '2026-06-10T00:23:35.577828Z'
source: https://arxiv.org/abs/2310.06770
source_domain: arxiv.org
fetched_at: '2026-06-10T00:23:35.577638Z'
fetch_provider: builtin
status: draft
type: note
tier: institutional
content_type: paper
deprecated: false
[2310.06770] SWE-bench: Can Language Models Resolve Real-World GitHub Issues? Computer Science > Computation and Language arXiv:2310.06770 (cs) [Submitted on 10 Oct 2023 ( v1 ), last revised 11 Nov 2024 (this version, v3)] Title: SWE-bench: Can Language Models Resolve Real-World GitHub Issues? Authors: Carlos E. Jimenez , John Yang , Alexander Wettig , Shunyu Yao , Kexin Pei , Ofir Press , Karthik Narasimhan View a PDF of the paper titled SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, by Carlos E. Jimenez and 6 other authors View PDF Abstract: Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we introduce SWE-bench, an evaluation framework consisting of $2,294$ software engineering problems drawn from real GitHub issues and corresponding pull requests across $12$ popular Python repositories. Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue. Resolving issues in SWE-bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks. Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues. The best-performing model, Claude 2, is able to solve a mere $1.96$% of the issues. Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous. Comments: Data, code, and leaderboard are available at this https URL ICLR 2024, this https URL Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2310.06770 [cs.CL] (or arXiv:2310.06770v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2310.06770 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Carlos E. Jimenez [ view email ] [v1] Tue, 10 Oct 2023 16:47:29 UTC (2,003 KB) [v2] Fri, 5 Apr 2024 18:16:29 UTC (2,258 KB) [v3] Mon, 11 Nov 2024 23:05:04 UTC (2,398 KB) Full-text links: Access Paper: View a PDF of the paper titled SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, by Carlos E. Jimenez and 6 other authors View PDF TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2023-10 Change to browse by: cs cs.AI cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )