Imbalanced data is a common problem in data mining when dealing with classification
problems, where samples of a class vastly outnumber other classes. In this situation, many …

Context: Software defect prediction (SDP) is an important challenge in the field of software
engineering, hence much research work has been conducted, most notably through the use …

Software defect prediction is important to identify defects in the early phases of software
development life cycle. This early identification and thereby removal of software defects is …

Software defect prediction performance varies over a large range. Menzies suggested there
is a ceiling effect of 80% Recall [8]. Most of the data sets used are highly imbalanced. This …

The data mining and machine learning community is often faced with two key problems:
working with imbalanced data and selecting the best features for machine learning. This …

The quality of the defect datasets is a critical issue in the domain of software defect
prediction (SDP). These datasets are obtained through the mining of software repositories …

Software systems are now ubiquitous and are used every day for automation purposes in
personal and enterprise applications; they are also essential to many safety-critical and …

Software defect prediction (SDP) is the technique used to predict the occurrences of defects
in the early stages of software development process. Early prediction of defects will reduce …

The method of recognizing faults in a software system is acknowledged as software fault
prediction. Software faults predicted in prior stages help in the management of resources …

To facilitate software testing, and save testing costs, a wide range of machine learning
methods have been studied to predict defects in software modules. Unfortunately, the …