Towards Causal Deep Learning for Vulnerability Detection. (arXiv:2310.07958v2 [cs.SE] UPDATED)

Deep learning vulnerability detection has shown promising results in recent
years. However, an important challenge that still blocks it from being very
useful in practice is that the model is not robust under perturbation and it
cannot generalize well over the out-of-distribution (OOD) data, e.g., applying
a trained model to unseen projects in real world. We hypothesize that this is
because the model learned non-robust features, e.g., variable names, that have
spurious correlations with labels. When the perturbed and OOD …

challenge data deep learning detection distribution important practice results under vulnerability vulnerability detection