Researchers have recently achieved significant advances in deep learning techniques,
which in turn has substantially advanced other research disciplines, such as natural …

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 …

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

Code review is effective, but human-intensive (eg, developers need to manually modify
source code until it is approved). Recently, prior work proposed a Neural Machine …

Various approaches are proposed to help under-resourced security researchers to detect
and analyze software vulnerabilities. It is still incredibly time-consuming and labor-intensive …

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

Story point estimation is a task to estimate the overall effort required to fully implement a
product backlog item. Various estimation approaches (eg, Planning Poker, Analogy, and …

DevOps has emerged as one of the most rapidly evolving software development paradigms.
With the growing concerns surrounding security in software systems, the DevSecOps …

Code review is an effective quality assurance practice, but can be labor-intensive since
developers have to manually review the code and provide written feedback. Recently, a …