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 Dempster-Shafer network by the induction algorithm; Second, selecting the predictors (attributes)...

Full description

Saved in:
Bibliographic Details
Published in18th IEEE International Conference on Automated Software Engineering, 2003. Proceedings Vol. 2003; pp. 249 - 252
Main Authors Guo, Lan, Cukic, Bojan, Singh, Harshinder
Format Conference Proceeding Journal Article
LanguageEnglish
Published Piscataway, NJ, USA IEEE Press 01.10.2003
IEEE
SeriesACM Conferences
Subjects
Accuracy
Classification tree analysis
Computing methodologies
Computing methodologies > Machine learning
Fault diagnosis
Lab-on-a-chip
Logistics
NASA
Power system modeling
Predictive models
Software quality
Statistics
Online AccessGet full text

Cover

More Information
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 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.
ISBN:0769520359
9780769520353
ISSN:1938-4300
1527-1366
2643-1572
DOI:10.1109/ASE.2003.1240314