Labeling a module defective or non-defective is an expensive task. Hence, there are often
limits on how much-labeled data is available for training. Semi-supervised classifiers use far …

Background Unsupervised machine learners have been increasingly applied to software
defect prediction. It is an approach that may be valuable for software practitioners because it …

Software defect prediction can automatically predict defect-prone software modules for
efficient software test in software engineering. When the previous defect labels of modules …

Software defect prediction is an important decision support activity in software quality
assurance. The limitation of the labelled modules usually makes the prediction difficult, and …

Software Defect Prediction (SDP) aims to assess software quality by using machine learning
techniques. Recently, by proposing the connectivity-based unsupervised learning method …

Many researchers assume that, for software analytics," more data is better." We write to show
that, at least for learning defect predictors, this may not be true. To demonstrate this, we …

Software defect prediction aims to automatically locate defective code modules to better
focus testing resources and human effort. Typically, software defect prediction pipelines are …

Predicting the defect-prone modules when the previous defect labels of modules are limited
is a challenging problem encountered in the software industry. Supervised classification …

Early software defect identification is considered an important step towards software quality
assurance. Software defect prediction aims at identifying software components that are likely …

Throughout its development period, a software project experiences different phases,
comprises modules with different complexities and is touched by many different developers …