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

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

Modern language models (LMs) have been successfully employed in source code
generation and understanding, leading to a significant increase in research focused on …

A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-
introducing. Recently, CC2Vec-a deep learning approach for Just-In-Time defect prediction …

Context: In recent years, leveraging machine learning (ML) techniques has become one of
the main solutions to tackle many software engineering (SE) tasks, in research studies …

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 …

Just-In-Time (JIT) defect prediction (ie, an AI/ML model to predict defect-introducing
commits) is proposed to help developers prioritize their limited Software Quality Assurance …

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

Ethics in AI has become a debated topic of public and expert discourse in recent years. But
what do people who build AI—AI practitioners—have to say about their understanding of AI …

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