Software defect prediction has caused widespread concern among software engineering
researchers, which aims to erect a software defect prediction model according to historical …

Software testing using software defect prediction aims to detect as many defects as possible
in software before the software release. This plays an important role in ensuring quality and …

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 …

The costs of finding and correcting software defects have been the most expensive activity in
software development. The accurate prediction of defect‐prone software modules can help …

Software defect prediction suffers from the class-imbalance. Solving the class-imbalance is
more important for improving the prediction performance. SMOTE is a useful over-sampling …

In practice, Software Defect Prediction (SDP) models often suffer from highly imbalanced
data, which makes classifiers difficult to identify defective instances. Recently, many …

Software defect prediction has gained considerable attention in recent years. A broad range
of computational methods has been developed for accurate prediction of faulty modules …

With the ever growing software industry, software defect prediction is one of the key
ingredients in recipe of producing good quality software. Defects uncovered well in time …

The use of machine learning techniques to predict software defects has received extensive
attention over the years. In practice, there are far fewer defective software samples than non …

Software defect prediction (SDP) plays a vital role in enhancing the quality of software
projects and reducing maintenance-based risks through the ability to detect defective …