There is a severe class imbalance problem in defect datasets, with nondefective data
dominating the distribution, making it easy to generate inaccurate software defect prediction …

Software defect prediction plays a crucial role in enhancing software quality while achieving
cost savings in testing. Its primary objective is to identify and send only defective modules to …

With the rapid development of technology, software projects are becoming increasingly
complex, but the problem of defects is still not well solved, and the application of defective …

Software defect prediction (SDP) is the process of predicting defects in software modules, it
identifies the modules that are defective and require extensive testing. Classification …

Defect prediction in software projects plays a crucial role to reduce quality-based risk and
increase the capability of detecting faulty program modules. Hence, classification …

Background: Ensemble techniques have gained attention in various scientific fields. Defect
prediction researchers have investigated many state-of-the-art ensemble models and …

Machine learning techniques are frequent for the complicated task of predicting software
defects. Often the prediction models fail to predict defects successfully as the between class …

This research highlights a procedure which includes a feature selection technique to single
out relevant attributes, and an ensemble technique to handle the class-imbalance issue. In …

Machine learning methods in software engineering are becoming increasingly important as
they can improve quality and testing efficiency by constructing models to predict defects in …

The class imbalance problem in software defect prediction datasets leads to prediction
results that are biased toward the majority class, and the class overlap problem leads to …