Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …

Software Engineering (SE) researchers are increasingly paying attention to organizational
and human factors. Rather than focusing only on variables that can be directly measured …

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 …

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is
nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or …

There is an increase in deploying Deep Learning (DL)-based software systems in real-world
applications. Usually, DL models are developed and trained using DL frameworks like …

The field of artificial intelligence (AI) is witnessing a recent upsurge in research, tools
development, and deployment of applications. Multiple software companies are shifting their …

Background. The increasing reliance on applications with machine learning (ML)
components calls for mature engineering techniques that ensure these are built in a robust …

While a large number of pre-trained models of source code have been successfully
developed and applied to a variety of software engineering (SE) tasks in recent years, our …

Software engineering (SE) research should be relevant to industrial practice. There have
been regular discussions in the SE community on this issue since the 1980's, led by …

Software analytics builds quality prediction models for software projects. Experience shows
that (a) the more projects studied, the more varied are the conclusions; and (b) project …