The performance of prediction models in software defect prediction depends on the quality
of datasets used for training such models. Class imbalance is one of data quality problems …

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 …

Class imbalance has drawn much attention of researchers in software defect prediction. In
practice, the performance of defect prediction models may be affected by the class …

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 …

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software
defect datasets tend to have fewer defective modules than non-defective modules. Synthetic …

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 …

Class imbalance is the potential problem that has been existent in machine learning, which
hinders the performance of the classification algorithm when applied in real-world …

Context: Generally, there are more non-defective instances than defective instances in the
datasets used for software defect prediction (SDP), which is referred to as the class …

Predicting the defects at early stage of software development life cycle can improve the
quality of end product at lower cost. Machine learning techniques have been proved to be …

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