{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pre-trained-model-based-automated-software","title":"Pre-trained Model-based Automated Software Vulnerability Repair: How Far are We?","arxiv_id":"2308.12533","date":"2023-08-24","proceeding":null,"authors":["Quanjun Zhang","Chunrong Fang","Bowen Yu","Weisong Sun","Tongke Zhang","Zhenyu Chen"],"abstract":"Various approaches are proposed to help under-resourced security researchers to detect and analyze software vulnerabilities. It is still incredibly time-consuming and labor-intensive for security researchers to fix vulnerabilities. The time lag between reporting and fixing a vulnerability causes software systems to suffer from significant exposure to possible attacks. Recently, some techniques have proposed applying pre-trained models to fix security vulnerabilities and have proved their success in improving repair accuracy. However, the effectiveness of existing pre-trained models has not been systematically analyzed, and little is known about their advantages and disadvantages. To bridge this gap, we perform the first extensive study on applying various pre-trained models to vulnerability repair. The results show that studied pre-trained models consistently outperform the state-of-the-art technique VRepair with a prediction accuracy of 32.94%~44.96%. We also investigate the impact of major phases in the vulnerability repair workflow. Surprisingly, a simplistic approach adopting transfer learning improves the prediction accuracy of pre-trained models by 9.40% on average. Besides, we provide additional discussion to illustrate the capacity and limitations of pre-trained models. Finally, we further pinpoint various practical guidelines for advancing pre-trained model-based vulnerability repair. Our study highlights the promising future of adopting pre-trained models to patch real-world vulnerabilities.","url_abs":"https://arxiv.org/abs/2308.12533v2","url_pdf":"https://arxiv.org/pdf/2308.12533v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"pre-trained-model-based-automated-software","repo_url":"https://github.com/isenglab/llm4vulfix","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2308.12533","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}