The high dimensionality of software metric features has long been noted as a data quality
problem that affects the performance of software defect prediction (SDP) models. This …

Selecting the most suitable filter method that will produce a subset of features with the best
performance remains an open problem that is known as filter rank selection problem. A …

Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and
many FS methods have been proposed in the context of software defect prediction (SDP) …

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 …

High dimensionality is a data quality problem that negatively influences the predictive
capabilities of prediction models in software defect prediction (SDP). As a viable solution …

Feature selection is known to be an applicable solution to address the problem of high
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …

With the variety of different filter methods, selecting the most appropriate filter method which
gives the best performance is a difficult task. Filter rank selection and stability problems …

Software Defect Prediction (SDP) is an approach used for identifying defect-prone software
modules or components. It helps software engineer to optimally, allocate limited resources to …

Finding defects early in a software system is a crucial task, as it creates adequate time for
fixing such defects using available resources. Strategies such as symmetric testing have …

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 …