title: Predicting Fault Prone Modules by the Dempster-Shafer Belief Networks.
abstract: This paper describes a novel methodology for predicting fault prone modules. The methodology is based on Dempster-Shafer (D-S) belief networks. Our approach consists of three steps: First, building the Dempster-Shafer network by the induction algorithm; Second, selecting the predictors (attributes) by the logistic procedure; Third, feeding the predictors describing the modules of the current project into the inducted Dempster-Shafer network and identifying fault prone modules. We applied this methodology to a NASA dataset. The prediction accuracy of our methodology is higher than that achieved by logistic regression or discriminant analysis on the same dataset.
authors.affiliation: Lane Department of CSEE West Virginia University Morgantown, West Virginia 26506-6109 lan@csee.wvu.edu.
authors.forename: Lan
authors.initials: L
authors.lastname: Guo
authors.affiliation: Lane Department of CSEE West Virginia University Morgantown, West Virginia 26506-6109 cukic@csee.wvu.edu.
authors.forename: Bojan
authors.initials: B
authors.lastname: Cukic
authors.affiliation: Department of Statistics West Virginia University Morgantown, West Virginia 26506-6330 hsingh@stat.wvu.edu.
authors.forename: Harshinder
authors.initials: H
authors.lastname: Singh