FVA: Assessing Function-Level Vulnerability by Integrating Flow-Sensitive Structure and Code Statement Semantic

Previous studies have been conducted on software vulnerability (SV) assessment at the code-based level, especially the function level. However, a key limitation of these studies is that they do not consider the structure information (e.g., control dependency and data dependency) of a vulnerable function, which is crucial for understanding SVs and assigning priority for fixing. In this study, we propose a flow-sensitive, multitask, and function-level vulnerability assessment method named FVA, which considers both global structure information and local semantic information. More specifically, FVA considers two types of flow information extracted from the control dependence graph and the data dependence graph. Meanwhile, FVA also considers the deep semantic information of the statement as well as its various types of contexts (i.e., surrounding context and program slicing context). We evaluate the effectiveness of FVA on the large-scale dataset (4,467 functions) by comparing it with four state-of-the-art baselines in terms of five performance measures. The experimental results indicate that FVA outperforms these baselines by a significant margin. More precisely, on average, FVA obtains 0.795 of F1-score and 0.727 of MCC, which improves baselines by 5%-14% and 8%-20%, respectively.