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 …

Abstract Natural Language Processing (NLP) has recently gained wide attention in
cybersecurity, particularly in Cyber Threat Intelligence (CTI) and cyber automation …

Deep learning-based vulnerability prediction approaches are proposed to help under-
resourced security practitioners to detect vulnerable functions. However, security …

Large language models (LLMs) have received considerable attention recently due to their
outstanding comprehension and reasoning capabilities, leading to great progress in many …

Abstract Many Machine Learning (ML)-based approaches have been proposed to
automatically detect, localize, and repair software vulnerabilities. While ML-based methods …

Cybercriminals relentlessly pursue vulnerabilities across cyberspace to exploit software,
threatening the security of individuals, organizations, and governments. Although security …

Software vulnerabilities have become a major problem for the security analysts, since the
number of new vulnerabilities is constantly growing. Thus, there was a need for a …

Software vulnerabilities are prevalent in software systems and the unresolved vulnerable
code may cause system failures or serious data breaches. To enhance security and prevent …

Nowadays, security testing is an integral part of the testing activities during the software
development life-cycle. Over the years, various techniques have been proposed to identify …

The thesis advances the field of software security by providing knowledge and automation
support for software vulnerability assessment using data-driven approaches. Software …