  Identifying vulnerabilities in the source code is essential to protect the
software systems from cyber security attacks. It, however, is also a
challenging step that requires specialized expertise in security and code
representation. To this end, we aim to develop a general, practical, and
programming language-independent model capable of running on various source
codes and libraries without difficulty. Therefore, we consider vulnerability
detection as an inductive text classification problem and propose ReGVD, a
simple yet effective graph neural network-based model for the problem. In
particular, ReGVD views each raw source code as a flat sequence of tokens to
build a graph, wherein node features are initialized by only the token
embedding layer of a pre-trained programming language (PL) model. ReGVD then
leverages residual connection among GNN layers and examines a mixture of
graph-level sum and max poolings to return a graph embedding for the source
code. ReGVD outperforms the existing state-of-the-art models and obtains the
highest accuracy on the real-world benchmark dataset from CodeXGLUE for
vulnerability detection. Our code is available at:
\url{https://github.com/daiquocnguyen/GNN-ReGVD}.
