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 …

Deep learning (DL) models of code have recently reported great progress for vulnerability
detection. In some cases, DL-based models have outperformed static analysis tools …

Software vulnerability has long been an important but critical research issue in
cybersecurity. Recently, the machine learning (ML)-based approach has attracted …

The growth of data volume has changed cybersecurity activities, demanding a higher level
of automation. In this new cybersecurity landscape, text mining emerged as an alternative to …

The detection of software vulnerabilities is considered a vital problem in the software
security area for a long time. Nowadays, it is challenging to manage software security due to …

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 …

A major cause of security incidents such as cyber attacks is rooted in software
vulnerabilities. These vulnerabilities should ideally be found and fixed before the code gets …

Open source software has been widely used in various industries due to its openness and
flexibility, but it also brings potential software security problems. Together with the large …

Open science is a practice that makes scientific research publicly accessible to anyone,
hence is highly beneficial. Given the benefits, the software engineering (SE) community has …