Background: The accurate prediction of where faults are likely to occur in code can help
direct test effort, reduce costs, and improve the quality of software. Objective: We investigate …

Software fault prediction (SFP) aims to improve software quality with a possible minimum
cost and time. Various machine learning models have been proposed in the past for …

Both the problem of class imbalance in datasets and parameter selection of Support Vector
Machine (SVM) are crucial to predict software defects. However, there is no one working to …

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 …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …

Defect models that are trained on class imbalanced datasets (ie, the proportion of defective
and clean modules is not equally represented) are highly susceptible to produce inaccurate …

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

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 …

Many defect prediction techniques are proposed to improve software reliability. Change
classification predicts defects at the change level, where a change is the modifications to …

Defect prediction on projects with limited historical data has attracted great interest from both
researchers and practitioners. Cross-project defect prediction has been the main area of …