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 DempsterShafer 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.

• Type: Conference
• Year: 2003
• Where: KBSE
• Authors: Lan Guo, Bojan Cukic, Harshinder Singh
• Keywords: Belief Networks, Fault Prone Modules, KBSE 2003, Methodology, Software Engineering