  Current machine-learning based software vulnerability detection methods are
primarily conducted at the function-level. However, a key limitation of these
methods is that they do not indicate the specific lines of code contributing to
vulnerabilities. This limits the ability of developers to efficiently inspect
and interpret the predictions from a learnt model, which is crucial for
integrating machine-learning based tools into the software development
workflow. Graph-based models have shown promising performance in function-level
vulnerability detection, but their capability for statement-level vulnerability
detection has not been extensively explored. While interpreting function-level
predictions through explainable AI is one promising direction, we herein
consider the statement-level software vulnerability detection task from a fully
supervised learning perspective. We propose a novel deep learning framework,
LineVD, which formulates statement-level vulnerability detection as a node
classification task. LineVD leverages control and data dependencies between
statements using graph neural networks, and a transformer-based model to encode
the raw source code tokens. In particular, by addressing the conflicting
outputs between function-level and statement-level information, LineVD
significantly improve the prediction performance without vulnerability status
for function code. We have conducted extensive experiments against a
large-scale collection of real-world C/C++ vulnerabilities obtained from
multiple real-world projects, and demonstrate an increase of 105\% in F1-score
over the current state-of-the-art.
