research/notes/231006770-swe-bench-can-language-models-resolve-real-world-github-issues.md

---
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
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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)
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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
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