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

Software security vulnerabilities are one of the critical issues in the realm of computer
security. Due to their potential high severity impacts, many different approaches have been …

The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …

This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities
in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation …

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

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 …

Because of rampant security breaches in IoT devices, searching vulnerabilities in massive
IoT ecosystems is more crucial than ever. Recent studies have demonstrated that control …

Software defect prediction, which predicts defective code regions, can assist developers in
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

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 …