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 …

This paper provides a survey of the emerging area of Large Language Models (LLMs) for
Software Engineering (SE). It also sets out open research challenges for the application of …

Unit tests play a key role in ensuring the correctness of software. However, manually
creating unit tests is a laborious task, motivating the need for automation. Large Language …

Many automated test generation techniques have been developed to aid developers with
writing tests. To facilitate full automation, most existing techniques aim to either increase …

Software security poses substantial risks to our society because software has become part of
our life. Numerous techniques have been proposed to resolve or mitigate the impact of …

Deep learning (DL) techniques are gaining more and more attention in the software
engineering community. They have been used to support several code-related tasks, such …

Testing plays a pivotal role in ensuring software quality, yet conventional Search Based
Software Testing (SBST) methods often struggle with complex software units, achieving …

In recent years, fuzz testing has proven itself to be one of the most effective techniques for
finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing …

Context: Test case generation is a critical yet challenging task in software development.
Recently, AthenaTest–a Deep Learning (DL) approach for generating unit test cases has …

Testing is widely recognized as an important stage of the software development lifecycle.
Effective software testing can provide benefits such as bug finding, preventing regressions …