Predicting fault prone modules by the Dempster-Shafer belief networks

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 D-S 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 D-S 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.