Over the last few decades, detecting surface defects has attracted significant attention as a
challenging task. There are specific classes of problems that can be solved using traditional …

The comprehensive intelligent development of the manufacturing industry puts forward new
requirements for the quality inspection of industrial products. This paper summarizes the …

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 …

Several prediction approaches are contained in the arena of software engineering such as
prediction of effort, security, quality, fault, cost, and re-usability. All these prediction …

Context: In practice, software datasets tend to have more non-defective instances than
defective ones, which is referred to as the class imbalance problem in software defect …

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 …

Abstract Support Vector Machine (SVM) has been widely used to build software defect
prediction models. Prior studies compared the accuracy of SVM to other machine learning …

Recent progress in Deep Learning (DL) has sparked interest in using DL to detect software
vulnerabilities automatically and it has been demonstrated promising results at detecting …

The class imbalance problem significantly hinders the ability of the software defect
prediction (SDP) models to distinguish between defective (minority class) and non-defective …

Context Interpretation has been considered as a key factor to apply defect prediction in
practice. As interpretation from rule-based interpretable models can provide insights about …