Skip to main content We gratefully acknowledge support from the Simons Foundation, member institutions , and all contributors. Donate > cs > arXiv:2410.06992 Help | Advanced Search All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text Search open search GO open navigation menu quick links Login Help Pages About --> Computer Science > Software Engineering arXiv:2410.06992 (cs) [Submitted on 9 Oct 2024 ( v1 ), last revised 10 Oct 2024 (this version, v2)] Title: SWE-Bench+: Enhanced Coding Benchmark for LLMs Authors: Reem Aleithan , Haoran Xue , Mohammad Mahdi Mohajer , Elijah Nnorom , Gias Uddin , Song Wang View a PDF of the paper titled SWE-Bench+: Enhanced Coding Benchmark for LLMs, by Reem Aleithan and 5 other authors View PDF HTML (experimental) Abstract: Large Language Models (LLMs) in Software Engineering (SE) can offer assistance for coding. To facilitate a rigorous evaluation of LLMs in practical coding contexts, Carlos et al. introduced the SWE-bench dataset, which comprises 2,294 real-world GitHub issues and their corresponding pull requests, collected from 12 widely used Python repositories. Several impressive LLM-based toolkits recently are developed and evaluated on this dataset. However, a systematic evaluation of the quality of SWE-bench remains missing. In this paper, we addressed this gap by presenting an empirical analysis of the SWE-bench dataset. We conducted a manual screening of instances where SWEAgent + GPT-4 successfully resolved issues by comparing the model-generated patches with the actual pull requests. SWE-Agent+GPT-4 was at the top of SWE-bench leaderboard during the time of our study. Our analysis reveals some critical issues with the SWE-bench dataset: 1) 32.67% of the successful patches involve cheating as the solutions were directly provided in the issue report or the comments. We refer to as solution leakage problem. 2) 31.08% of the passed patches are suspicious patches due to weak test cases, i.e., the tests were not adequate to verify the correctness of a patch. When we filtered out these problematic issues, the resolution rate of SWE-Agent+GPT-4 dropped from 12.47% to 3.97%. We also observed that the same data quality issues also exist in the two variants of SWE-bench, i.e., SWE-bench Lite and SWE-Bench Verified. In addition, over 94% of the issues were created before LLM's knowledge cutoff dates, posing potential data leakage issues. Subjects: Software Engineering (cs.SE) Cite as: arXiv:2410.06992 [cs.SE] (or arXiv:2410.06992v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2410.06992 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Song Wang [ view email ] [v1] Wed, 9 Oct 2024 15:38:53 UTC (3,863 KB) [v2] Thu, 10 Oct 2024 13:13:09 UTC (1,714 KB) Full-text links: Access Paper: View a PDF of the paper titled SWE-Bench+: Enhanced Coding Benchmark for LLMs, by Reem Aleithan and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2024-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... 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