Predicting fault prone modules by the Dempster-Shafer belief networks
Guo, L.; Cukic, B.; Singh, H.
Automated Software Engineering, 2003. Proceedings. 18th IEEE International Conference on
Volume , Issue , 6-10 Oct. 2003 Page(s): 249 - 252
Digital Object Identifier   10.1109/ASE.2003.1240314
Summary: 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.

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