Abstract:Predicting software defects is an important task during software testing phase, especially for allocating appropriate resources and prioritizing testing tasks. Typically, classification algorithms are used to accomplish this task by using previously collected datasets. However, these datasets suffer from imbalanced label distribution where clean modules outnumber defective modules. Traditional classification algorithms cannot handle this nature in defect datasets because they assume the datasets are balanced. … Show more
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