A Novel Feature Subset Selection Algorithm for Software Defect Prediction

Abstract

Feature subset selection is the process of choosing a subset of good features with respect to the target concept.A clustering based feature subset selection algorithm has been applied over software defect prediction data sets.Software defect prediction domain has been chosen due to the growing importance of maintaining high reliability and high quality for any software being developed.A software quality prediction model is built using software metrics and defect data collected from a previously developed system release or similar software projects.Upon validation of such a model, it could be used for predicting the fault-proneness of program modules that are currently under development.The proposed clustering based algorithm for feature selection uses minimum spanning tree based method to cluster features.And then the algorithm is applied over four different data sets and its impact is analyzed.