关键词: 漏洞检测, 边权重, 图表示学习

Abstract: With the widespread application of software across various fields, software vulnerabilities have shown a continuous growth trend, posing a serious threat to system security. As a result, software vulnerability detection has become one of the research hotspots in the field of software engineering. In recent years, deep learning-based methods for software vulnerability detection have been widely adopted. Among these, graph representation learning-based methods have garnered significant attention from researchers, and their effectiveness has been proven. However, existing graph representation learning methods still have certain limitations, particularly in accurately determining the importance of unweighted edges in graphs, which hinders the model's ability to learn the potential relationships among vulnerability statements. To address this issue, this paper proposes a software vulnerability detection method based on edge weights (Edge Weight for Vulnerability Detection, EWVD). This method effectively identifies the information transmission capability of nodes by combining three metrics: connection structure, the importance of neighboring nodes, and Jaccard similarity, thereby improving vulnerability detection performance. Compared to the best-performing baseline among seven vulnerability detection baseline methods, the proposed EWVD method achieves an average improvement of 1.06% in Accuracy and an average reduction of 1.11% in FPR.

Key words: vulnerability detection, edge weight, graph representation learning