Software defect prediction has recently attracted attention of many software quality
researchers. One of the major areas in current project management software is to effectively …

Abstract The performance of Software Defect Prediction (SDP) models depends on the
quality of dataset used for training the models. The high dimensionality of software metric …

Software development involves plenty of risks, and errors exist in software modules
represent a major kind of risk. Software defect prediction techniques and tools that identify …

Predicting defect-prone software modules accurately and effectively is an important way to
control the quality of a software system during software development. Feature selection can …

Naïve Bayes is one of the most simplest and efficient classification algorithms and is
therefore being widely used. It has been utilized in several situations, like web mining …

Directly learning a defect prediction model from cross-project datasets results in a model
with poor performance. Hence, training data selection becomes a feasible solution to this …

Software defect prediction (SDP) is used to perform the statistical analysis of historical defect
data to find out the distribution rule of historical defects, so as to effectively predictdefects in …

Software defect prediction strives to improve software quality and testing efficiency by
constructing predictive classification models from code attributes to enable a timely …

An improved software defects prediction algorithm based on combination of Kohonen map
and hierarchical clustering is presented in this paper. The need for software reliability …

Early detection of defective software components enables verification experts give much
time and allocate scare resources to the problem areas of the system under development …