Attacks against computer systems exploiting software vulnerabilities can cause substantial damage to the cyber-infrastructure of our modern society and economy. To minimize the consequences, it is vital to detect and fix vulnerabilities as soon as possible. Just-in-time vulnerability detection (JIT-VD) discovers vulnerability-prone ("dangerous") commits to prevent them from being merged into source code and causing vulnerabilities. By JIT-VD, the commits' authors, who understand the commits properly, can review these dangerous commits and fix them if necessary while the relevant modifications are still fresh in their minds. In this paper, we propose CodeJIT, a novel code-centric learning-based approach for just-in-time vulnerability detection. The key idea of CodeJIT is that the meaning of the code changes of a commit is the direct and deciding factor for determining if the commit is dangerous for the code. Based on that idea, we design a novel graph-based representation to represent the semantics of code changes in terms of both code structures and program dependencies. A graph neural network model is developed to capture the meaning of the code changes represented by our graph-based representation and learn to discriminate between dangerous and safe commits. We conducted experiments to evaluate the JIT-VD performance of CodeJIT on a dataset of 20K+ dangerous and safe commits in 506 real-world projects from 1998 to 2022. Our results show that CodeJIT significantly improves the state-of-the-art JIT-VD methods by up to 66% in Recall, 136% in Precision, and 68% in F1. Moreover, CodeJIT correctly classifies nearly 9/10 of dangerous/safe (benign) commits and even detects 69 commits that fix a vulnerability yet produce other issues in source code
A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Tasks
Institute of Al for Industries, Chinese Academy of Sciences（中国科学院工业人工智能研究所）；Northeastern University（东北大学）；University of Science and Technology of China（中国科学技术大学）
A systematic literature Review for Transformer-based Software Vulnerability detection
Department of Computer Science, Cybersecurity and Computing Systems Research Group, University of Hertfordshire, Hertfordshire（计算机科学系、网络安全与计算系统研究组、赫特福德郡大学、赫特福德郡）；Department of Software Engineering, Obafemi Awolowo University Ile-Ife（软件工程系、奥法米-阿沃洛沃大学伊费）
