Reimplement partial implementation of Devign, a graph neural network-based model for identifying vulnerabilities in C code. Includes tools for data preparation, Joern integration, and model training for vulnerability detection.
Reimplement empirical study on deep learning-based vulnerability detection techniques using real-world datasets (Devign and Chrome+Debian). Includes tools for parsing, slicing, and analyzing C code with GGNN and ReVeal pipelines.