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Predicting Fault Prone Modules by the Dempster-Shafer Belief Networks
Lan Guo1, Bojan Cukic2, Harshinder Singh3
1Lane Department of CSEE West Virginia University Morgantown, West Virginia 26506-6109 lan@csee.wvu.edu.
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.
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