This paper provides a comprehensive review of the literature concerning the utilization of
Natural Language Processing (NLP) techniques, with a particular focus on transformer …

Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

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 …

In this work we systematically review the recent advancements in software engineering with
language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 …

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 …

Driven by the increasing scale and high profile cybersecurity incidents related public data,
recent years we have witnessed a paradigm shift in understanding and defending against …

Automatically detecting software vulnerabilities is an important problem that has attracted
much attention from the academic research community. However, existing vulnerability …

With the increase in software vulnerabilities that cause significant economic and social
losses, automatic vulnerability detection has become essential in software development and …

Defect prediction is proposed to assist practitioners effectively prioritize limited Software
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …