Automated code review and vulnerability detection using graph neural networks: Enhancing DevSecOps Workflows

Modern software development practices increasingly emphasize security integration throughout the development lifecycle, particularly in DevSecOps workflows. This research proposes a novel approach to automated code review and vulnerability detection using Graph Neural Networks (GNNs), which represent code as structural graphs to capture semantic relationships between code elements. We developed a comprehensive framework that converts source code into graph representations, extracts semantic features, and trains GNN models to identify security vulnerabilities and code quality issues. Our model achieved 93.7% accuracy in vulnerability detection across multiple programming languages, outperforming traditional static analysis tools by 27% and conventional deep learning approaches by 18%. The system was integrated into CI/CD pipelines to provide real-time feedback during code commits, reducing security vulnerabilities by 76% and decreasing false positives by 41% compared to conventional methods. Our approach demonstrates significant improvements in detection accuracy, context-awareness, and reduction in manual review time, offering a promising direction for enhancing security in modern software development environments.