Pattern analysis often requires a pre-processing stage for extracting or selecting features in
order to help the classification, prediction, or clustering stage discriminate or represent the …

Software fault prediction aims to identify fault-prone software modules by using some
underlying properties of the software project before the actual testing process begins. It …

The selection of software metrics for building software quality prediction models is a search‐
based software engineering problem. An exhaustive search for such metrics is usually not …

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 …

Software fault prediction (SFP) aims to improve software quality with a possible minimum
cost and time. Various machine learning models have been proposed in the past for …

The performance of a defect classification model depends on the features that are used to
train it. Feature redundancy, correlation, and irrelevance can hinder the performance of a …

Defect prediction is an important task for preserving software quality. Most prior work on
defect prediction uses software features, such as the number of lines of code, to predict …

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 Effort‐Aware Defect Prediction (EADP) methods sort software modules based on
the defect density and guide the testing team to inspect the modules with high defect density …

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 …