Deep learning (DL), a branch of machine learning (ML), is the core technology in today's
technological advancements and innovations. Deep learning-based approaches are the …

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 …

This study discusses the effects of class imbalance and training data size on the predictive
performance of classifiers. An empirical study was performed on ten classifiers arising from …

One of the most important topics in machine learning is how to evaluate the models, which
means measuring how accurately they predict the expected outcome. In addition to …

The quality of the software being developed varies with the size and complexity of the
software. It is a matter of concern in software development as it impairs the faith of customers …

The occurrence of faults in software systems represents an inevitable predicament. Testing
is the most common means to detect such faults; however, exhaustive testing is not feasible …

Software is a complex entity, and its development needs careful planning and a high amount
of time and cost. To assess quality of program, software measures are very helpful. Amongst …

Recently, software project failures have been increasing due to lack of planning and budget
constraints. In this regard, identifying the suitable software model with the consideration of …

Context: Despite a large amount of literature on Software Defect Prediction (SDP), its
industrial applications are rarely reported and validated in vivo. Objective: We aim to: 1) …

The attempt to concretely define the concept of explainability in terms of other vaguely
described notions, is doomed to fail. We argue that a rigorous numeric measure is needed to …