An Empirical Study of Deep Learning Models for Vulnerability Detection. (arXiv:2212.08109v1 [cs.SE])

Deep learning (DL) models of code have recently reported great progress for
vulnerability detection. In some cases, DL-based models have outperformed
static analysis tools. Although many great models have been proposed, we do not
yet have a good understanding of these models. This limits the further
advancement of model robustness, debugging, and deployment for the
vulnerability detection. In this paper, we surveyed and reproduced 9
state-of-the-art (SOTA) deep learning models on 2 widely used vulnerability
detection datasets: Devign and MSR. …

deep learning detection study vulnerability vulnerability detection