Context: Machine learning software defect prediction is a promising field of software
engineering, attracting a great deal of attention from the research community; however, its …

Context: 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 …

Increasing the impact of software engineering research in the software industry and the
society at large has long been a concern of high priority for the software engineering …

Context: Software regression testing refers to rerunning test cases after the system under
test is modified, ascertaining that the changes have not (re-) introduced failures. Not all …

Continuous integration at scale is costly but essential to software development. Various test
optimization techniques including test selection and prioritization aim to reduce the cost …

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 …

Software testing is a crucial component of software development. With the increasing
complexity of software systems, traditional manual testing methods are becoming less …

Test case prioritization (TCP) aims to reorder the regression test suite with a goal of
increasing the fault detection rate. Various TCP techniques have been proposed based on …

Continuous Integration (CI) has become a well-established software development practice
for automatically and continuously integrating code changes during software development …

The task of ordering classes reasonably in the context of integration testing has been
discussed by many researchers. Existing methods regard the class integration test order …