Pre-trained Model-based Automated Software Vulnerability Repair: How Far are We?

• URL: http://arxiv.org/abs/2308.12533v2
• Date: Fri, 25 Aug 2023 01:34:33 GMT
• Title: Pre-trained Model-based Automated Software Vulnerability Repair: How Far are We?
• Authors: Quanjun Zhang, Chunrong Fang, Bowen Yu, Weisong Sun, Tongke Zhang, Zhenyu Chen
• Abstract summary: We show that studied pre-trained models consistently outperform the state-of-the-art technique VRepair with a prediction accuracy of 32.94%44.96%. Surprisingly, a simplistic approach adopting transfer learning improves the prediction accuracy of pre-trained models by 9.40% on average. Our study highlights the promising future of adopting pre-trained models to patch real-world vulnerabilities.
• Score: 14.741742268621403
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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