Enhancing Code Vulnerability Detection through Fine-Grained Modeling and Vulnerability-Preserving Data Augmentation

This work proposes FGVulDet, a fine-grained vulnerability detector that employs multiple classifiers to discern characteristics of various vulnerability types and combines their outputs to identify the specific type of vulnerability. Additionally, it introduces a novel vulnerability-preserving data augmentation technique to enrich the diversity of the training data.