Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

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

Security vulnerabilities in modern software are prevalent and harmful. While automated
vulnerability detection tools have made promising progress, their scalability and applicability …

This paper proposes a pipeline for quantitatively evaluating interactive Large Language
Models (LLMs) using publicly available datasets. We carry out an extensive technical …

The widespread use of open-source software (OSS) has led to extensive code reuse,
making vulnerabilities in OSS significantly pervasive. The vulnerabilities due to code reuse …

According to our survey of the machine learning for vulnerability detection (ML4VD)
literature published in the top Software Engineering conferences, every paper in the past 5 …

DevOps has emerged as one of the most rapidly evolving software development paradigms.
With the growing concerns surrounding security in software systems, the DevSecOps …

Recent advancements in large language models (LLMs) have highlighted the potential for
vulnerability de-tection, a crucial component of software quality assurance. Despite this …

This study provides a comparative analysis of state-of-the-art large language models
(LLMs), analyzing how likely they generate vulnerabilities when writing simple C programs …

Transformer based code models have impressive performance in many software
engineering tasks. However, their effectiveness degrades when symbols are missing or not …