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

The automation of code review has been tackled by several researchers with the goal of
reducing its cost. The adoption of deep learning in software engineering pushed the …

Transformers have gained popularity in the software engineering (SE) literature. These deep
learning models are usually pre-trained through a self-supervised objective, meant to …

Code review is an essential activity for ensuring the quality and maintainability of software
projects. However, it is a time-consuming and often error-prone task that can significantly …

The automation of code review activities, a long-standing pursuit in software engineering,
has been primarily addressed by numerous domain-specific pre-trained models. Despite …

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

Learning-based techniques, especially advanced Large Language Models (LLMs) for code,
have gained considerable popularity in various software engineering (SE) tasks. However …

Developers often perform repetitive code editing activities (up to 70%) for various reasons
(eg, code refactoring) during software development. Many deep learning (DL) models have …

Code review is an effective software quality assurance activity; however, it is labor-intensive
and time-consuming. Thus, a number of generation-based automatic code review (ACR) …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …