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

Software defect prediction refers to the automatic identification of defective parts of software
through machine learning techniques. Ensemble learning has exhibited excellent prediction …

Two important problems which can affect the performance of classification models are high-
dimensionality (an overabundance of independent features in the dataset) and imbalanced …

Abstract Software Fault Prediction (SFP) research has made enormous endeavor to
accurately predict fault proneness of software modules, thus maximize precious software test …

One of the most active areas of research in the software engineering community is defect
prediction. The gap between data mining and software engineering must be bridged to …

Software defect prediction models are used for predicting high risk software components.
Feature selection has significant impact on the prediction performance of the software defect …

Software defect prediction is aimed to find potential defects based on historical data and
software features. Software features can reflect the characteristics of software modules …

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 …

Software Defect Prediction (SDP) models are built using software metrics derived from
software systems. The quality of SDP models depends largely on the quality of software …

Defect prediction at early stages of software development life cycle is a crucial activity of
quality assurance process and has been broadly studied in the last two decades. The early …