While some areas of software engineering knowledge present great advances with respect
to the automation of processes, tools, and practices, areas such as software maintenance …

Background: As Machine Learning (ML) advances rapidly in many fields, it is being adopted
by academics and businesses alike. However, ML has a number of different challenges in …

Artificial intelligence (AI) and machine learning (ML) is becoming commonplace in
numerous fields. As they are often embedded in the context of larger software systems …

The way practitioners perform maintenance tasks in practice is little known by researchers.
In turn, practitioners are not always up to date with the proposals provided by the research …

The growing competitiveness of the market, coupled with the increase in automation driven
with the advent of Industry 4.0, highlights the importance of maintenance within …

Research studies in software maintenance are notoriously hard to reproduce due to lack of
datasets, tools, implementation details (eg, parameter values, environmental settings) and …

Context: The software development industry is rapidly adopting machine learning for
transitioning modern day software systems towards highly intelligent and self-learning …

The increasing availability of data, gathered by sensors and intelligent machines, is
changing the way decisions are made in the manufacturing sector. In particular, based on …

Software maintainability is a prime trait of software, measured as the ease with which new
code lines can be added, obsolete ones can be deleted, and those having errors can be …

In the realm of software development, ensuring optimal performance and reliability of
applications is paramount. Traditional approaches to maintenance often involve reactive …