VCMatch: A Ranking-based Approach for Automatic Security Patches Localization for OSS Vulnerabilities

Abstract

Nowadays, vulnerabilities in open source software (OSS) are constantly emerging, posing a great threat to application security. Security patches are crucial in reducing the risk of OSS vulnerabilities. However, many of the vulnerabilities disclosed by CVE/NVD are not accompanied by security patches. Previous research has shown that the auxiliary information in CVE/NVD can aid in the matching of a vulnerability to appropriate commits. The state-of-art research proposed a rank-based approach based on the multiple dimensions of features extracted from the auxiliary information in CVE/NVD. However, this approach ignores the semantic features in the vulnerability descriptions and commit messages, making the model still have room for improvement. In this paper, we propose a novel ranking-based approach VCMATCH (Vulnerability-Commit Match). In addition to extracting the shallow statistical features between the vulnerability and the patch commit, VCMATCH extracts the deep semantic features of the vulnerability descriptions and commit messages. Besides, VCMATCH applies three classification models (i.e., XGBoost, LightGBM, CNN) and uses a voting-based rank fusion method to combine the results of the three models to generate a better result. We evaluate VCMATCH with 1,669 CVEs from 10 OSS projects. The experiment results show that VCMATCH can effectively identify security patches for OSS vulnerabilities in terms of Recall@K and Manual Effort@K, and outperforms the state-of-art model by a statistically significant margin.