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VulCurator: A Vulnerability-Fixing Commit Detector

Authors :
Nguyen, Truong Giang
Le-Cong, Thanh
Kang, Hong Jin
Le, Xuan-Bach D.
Lo, David
Publication Year :
2022

Abstract

Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, including commit messages, code changes and issue reports for vulnerability-fixing commit classifica- tion. Our experimental results show that VulCurator outperforms the state-of-the-art baselines up to 16.1% in terms of F1-score. VulCurator tool is publicly available at https://github.com/ntgiang71096/VFDetector and https://zenodo.org/record/7034132#.Yw3MN-xBzDI, with a demo video at https://youtu.be/uMlFmWSJYOE.<br />Comment: accepted to ESEC/FSE 2022, Tool Demos Track

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2209.03260
Document Type :
Working Paper