The main problem in producing high accuracy software defect prediction is if the data set
has an imbalance class and dichotomous characteristics. The imbalanced class problem …

Defects can cause significant software rework, delays, and high costs, to prevent disability it
must be predictable the possibility of defects. To predict the disability the metrics software …

Predicting the defects at early stage of software development life cycle can improve the
quality of end product at lower cost. Machine learning techniques have been proved to be …

Software defect prediction using data mining techniques is one of the best practices for
finding defective modules. The existing classification techniques can be used for efficient …

With the growing rate of software systems and their applications in diverse walks of life,
developing a software system that has no defects is a subject that cannot be …

Data imbalance is one among characteristics of software quality data sets that can have a
negative effect on the performance of software defect prediction models. This study …

Objectives: To propose a suitable imbalanced data classification model to split the dataset
into two new datasets and to test the created imbalanced dataset by the prediction models …

Traditional statistical learning algorithms perform poorly in case of learning from an
imbalanced dataset. Software defect prediction (SDP) is a useful way to identify defects in …

The cost of finding and correcting the software defects are high and increases exponentially
in the software development. The software defect prediction (SDP) can be used in the early …

The most frequently used machine learning feature ranking approaches failed to present
optimal feature subset for accurate prediction of defective software modules in out-of-sample …