Predicting fault prone modules by the Dempster-Shafer belief networks

Lan Guo; Bojan Cukic; Harshinder Singh

doi:10.1109/ASE.2003.1240314

Predicting fault prone modules by the Dempster-Shafer belief networks

Lan Guo
, Bojan Cukic
, Harshinder Singh

School of Computing

Binghamton University

West Virginia University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

65 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	249-252
Number of pages	4
ISBN (Electronic)	0769520359, 9780769520353
DOIs	https://doi.org/10.1109/ASE.2003.1240314
State	Published - 2003
Event	18th IEEE International Conference on Automated Software Engineering, ASE 2003 - Montreal, Canada Duration: Oct 6 2003 → Oct 10 2003

Publication series

Name	Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003

Conference

Conference	18th IEEE International Conference on Automated Software Engineering, ASE 2003
Country/Territory	Canada
City	Montreal
Period	10/6/03 → 10/10/03

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10.1109/ASE.2003.1240314

Cite this

Guo, L., Cukic, B., & Singh, H. (2003). Predicting fault prone modules by the Dempster-Shafer belief networks. In Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003 (pp. 249-252). (Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASE.2003.1240314

Guo, Lan ; Cukic, Bojan ; Singh, Harshinder. / Predicting fault prone modules by the Dempster-Shafer belief networks. Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003. Institute of Electrical and Electronics Engineers Inc., 2003. pp. 249-252 (Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003).

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title = "Predicting fault prone modules by the Dempster-Shafer belief networks",

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.",

author = "Lan Guo and Bojan Cukic and Harshinder Singh",

note = "Publisher Copyright: {\textcopyright} 2003 IEEE; 18th IEEE International Conference on Automated Software Engineering, ASE 2003 ; Conference date: 06-10-2003 Through 10-10-2003",

year = "2003",

doi = "10.1109/ASE.2003.1240314",

language = "English",

series = "Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003",

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Guo, L, Cukic, B & Singh, H 2003, Predicting fault prone modules by the Dempster-Shafer belief networks. in Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003. Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003, Institute of Electrical and Electronics Engineers Inc., pp. 249-252, 18th IEEE International Conference on Automated Software Engineering, ASE 2003, Montreal, Canada, 10/6/03. https://doi.org/10.1109/ASE.2003.1240314

Predicting fault prone modules by the Dempster-Shafer belief networks. / Guo, Lan; Cukic, Bojan; Singh, Harshinder.
Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003. Institute of Electrical and Electronics Engineers Inc., 2003. p. 249-252 (Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2003

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N2 - 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.

AB - 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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DO - 10.1109/ASE.2003.1240314

M3 - Conference contribution

T3 - Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003

SP - 249

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BT - Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003

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Guo L, Cukic B, Singh H. Predicting fault prone modules by the Dempster-Shafer belief networks. In Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003. Institute of Electrical and Electronics Engineers Inc. 2003. p. 249-252. (Proceedings - 18th IEEE International Conference on Automated Software Engineering, ASE 2003). doi: 10.1109/ASE.2003.1240314

Predicting fault prone modules by the Dempster-Shafer belief networks

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