The detection of software vulnerability requires critical attention during the development
phase to make it secure and less vulnerable. Vulnerable software always invites hackers to …

Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security
risks to many software systems. Given the limited resources in practice, SV assessment and …

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 …

Since deep learning (DL) can automatically learn features from source code, it has been
widely used to detect source code vulnerability. To achieve scalable vulnerability scanning …

Automatically detecting software vulnerabilities is an important problem that has attracted
much attention from the academic research community. However, existing vulnerability …

The large transformer-based language models demonstrate excellent performance in
natural language processing. By considering the transferability of the knowledge gained by …

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine
learning (ML) pipelines. We systematize the increasingly growing (but fragmented) …

In recent years, numerous Machine Learning (ML) models, including Deep Learning (DL)
and classic ML models, have been developed to detect software vulnerabilities. However …

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated
software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in …

Vulnerability detection is essential to protect software systems. Various approaches based
on deep learning have been proposed to learn the pattern of vulnerabilities and identify …