Automated Program Repair (APR) attempts to patch software bugs and reduce manual
debugging efforts. Very recently, with the advances in Large Language Models (LLMs), an …

This paper provides a comprehensive review of the future of cybersecurity through
Generative AI and Large Language Models (LLMs). We explore LLM applications across …

Software vulnerabilities are prevalent in software systems, causing a variety of problems
including deadlock, information loss, or system failures. Thus, early predictions of software …

Current machine-learning based software vulnerability detection methods are primarily
conducted at the function-level. However, a key limitation of these methods is that they do …

We propose and release a new vulnerable source code dataset. We curate the dataset by
crawling security issue websites, extracting vulnerability-fixing commits and source codes …

Despite the successes of machine learning (ML) and deep learning (DL)-based vulnerability
detectors (VD), they are limited to providing only the decision on whether a given code is …

As software vulnerabilities grow in volume and complexity, researchers proposed various
Artificial Intelligence (AI)-based approaches to help under-resourced security analysts to …

Security vulnerability repair is a difficult task that is in dire need of automation. Two groups of
techniques have shown promise:(1) large code language models (LLMs) that have been pre …

Large language models (large LMs) are increasingly trained on massive codebases and
used to generate code. However, LMs lack awareness of security and are found to …

The use of learning-based techniques to achieve automated software vulnerability detection
has been of longstanding interest within the software security domain. These data-driven …