Recent advances in the domain of software defect prediction (SDP) include the integration of
multiple classification techniques to create an ensemble or hybrid approach. This technique …

Delivering high-quality software products is a challenging task. It needs proper coordination
from various teams in planning, execution, and testing. Many software products have high …

This paper studies the classification of unbalanced data sets. First, this kind of data sets is
briefly introduced, and then the classification methods of unbalanced data sets are analyzed …

Abstract Software Defect Prediction (SDP) is highly crucial task in software development
process to forecast about which modules are more prone to errors and faults before the …

Defect prediction models-classifiers that identify defect-prone software modules-have
configurable parameters that control their characteristics (eg, the number of trees in a …

Several prediction approaches are contained in the arena of software engineering such as
prediction of effort, security, quality, fault, cost, and re-usability. All these prediction …

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software
defect datasets tend to have fewer defective modules than non-defective modules. Synthetic …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

Software systems have become larger and more complex than ever. Such characteristics
make it very challengeable to prevent software defects. Therefore, automatically predicting …

Defect prediction models are classifiers that are trained to identify defect-prone software
modules. Such classifiers have configurable parameters that control their characteristics (eg …