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

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

Machine learning and its promising branch deep learning have proven to be effective in a
wide range of application domains. Recently, several efforts have shown success in …

Recently, automated vulnerability repair approaches have been widely adopted to combat
increasing software security issues. In particular, transformer-based encoder-decoder …

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

The explosive growth of software vulnerabilities poses a serious threat to the system security
and has become one of the urgent problems of the day. However, existing vulnerability …

Many studies have developed Machine Learning (ML) approaches to detect Software
Vulnerabilities (SVs) in functions and fine-grained code statements that cause such SVs …

Over the years, open-source software systems have become prey to threat actors. Even
highly-adopted software has been crippled by unforeseeable attacks, leaving millions of …

In recent years, deep learning (DL)-based methods have been widely used in code
vulnerability detection. The DL-based methods typically extract structural information from …

Interpretable machine learning seeks to understand the reasoning process of complex black-
box systems that are long notorious for lack of explainability. One flourishing approach is …