Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …

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 …

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 …

Software vulnerabilities inflict considerable economic and societal harm. Therefore, timely
and accurate detection of these flaws has become vital. Large language models (LLMs) …

This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …

The automatic detection of software vulnerabilities is an important research problem.
However, existing solutions to this problem rely on human experts to define features and …

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 …

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

Fine-grained software vulnerability detection is an important and challenging problem.
Ideally, a detection system (or detector) not only should be able to detect whether or not a …