Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and
many FS methods have been proposed in the context of software defect prediction (SDP) …

Selecting the most suitable filter method that will produce a subset of features with the best
performance remains an open problem that is known as filter rank selection problem. A …

Finding defects early in a software system is a crucial task, as it creates adequate time for
fixing such defects using available resources. Strategies such as symmetric testing have …

Feature selection is known to be an applicable solution to address the problem of high
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …

High dimensionality and class imbalance are latent data quality problems that have a
negative effect on the predictive capabilities of prediction models in software defect …

Multi-classifier systems (MCSs) are some kind of predictive models that classify instances by
combining the output of an ensemble of classifiers given in a pool. With the aim of …

This research work investigates the deployment of data sampling and ensemble techniques
in alleviating the class imbalance problem in software defect prediction (SDP). Specifically …

With the growing rate of software systems and their applications in diverse walks of life,
developing a software system that has no defects is a subject that cannot be …

The high dimensionality of software metric features has long been noted as a data quality
problem that affects the performance of software defect prediction (SDP) models. This …

The signifier is regarded as a crucial part of interface design since it ensures that the user
can manage the device appropriately and understand the interaction that is taking place …