Background The quest for higher software quality remains a paramount concern in software
testing, prompting a shift towards leveraging machine learning techniques for enhanced …

Abstract Context: Continuous Integration (CI) environments allow frequent integration of
software changes, making software evolution more rapid and cost-effective. In such …

Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights
in specific phases. The rise of continuous practices in software projects emphasizes …

To enter the production stage, in DevOps practices candidate software releases have to
pass quality gates, where they are assessed to meet established target values for key …

There is a growing body of research indicating the potential of machine learning to tackle
complex software testing challenges. One such challenge pertains to continuous integration …

Technology advances faster than ever in the current era, requiring global software
companies to work in an environment of fast‐paced change. In today's booming software …

In the last decade, the research community has been actively working to develop the
techniques that can automatically find a solution to a software fault, namely, automatic …

One of the significant features of software quality is software reliability. In the testing phase,
faults are identified and corrected by integrating them into software development, thus …

Traditionally, given a test-suite and the underlying system-under-test, existing test-case
prioritization heuristics report a permutation of the original test-suite that is seemingly best …

Component-based software development is rapidly introducing numerous new paradigms
and possibilities to deliver highly customized software in a distributed environment. Among …