Large language models (LLMs) like ChatGPT (ie, gpt-3.5-turbo and gpt-4) exhibited
remarkable advancement in a range of software engineering tasks associated with source …

The significant advancements in Large Language Models (LLMs) have resulted in their
widespread adoption across various tasks within Software Engineering (SE), including …

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

Software vulnerabilities inflict considerable economic and societal harm. Therefore, timely
and accurate detection of these flaws has become vital. Large language models (LLMs) …

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 …

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 …

Previous learning-based vulnerability detection methods relied on either medium-sized pre-
trained models or smaller neural networks from scratch. Recent advancements in Large Pre …

In the context of the rising interest in code language models (code LMs) and vulnerability
detection, we study the effectiveness of code LMs for detecting vulnerabilities. Our analysis …

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 …

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated
software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in …