Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation

arXiv:2404.15687v1 Announce Type: cross
Abstract: Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainability due to their inherently black-box nature. To this end, several factual reasoning-based explainers have been proposed. These explainers provide explanations for the predictions made by GNNs …

arxiv capture code cs.ai cs.cr cs.se detection graph networks neural networks reliability security semantic software software systems source code structure systems vulnerability vulnerability detection