Combining Graph-based Learning with Automated
Data Collection for Code Vulnerability Detection
FUNDED leverages the advances in graph neural networks (GNNs) to develop a novel graph-based learning method to capture and
reason about the program’s control, data, and call dependencies. FUNDED learns and operates on a graph representation of the program source code, in which
individual statements are connected to other statements through relational edges. By capturing the program syntax, semantics
and flows, FUNDED finds better code representation for the downstream software vulnerability detection task.