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Related Experiment Video

Updated: Apr 8, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

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.

Proceedings. IEEE International Automated Software Engineering Conference

|June 30, 2015

Summary

This study introduces a new method using Dempster-Shafer (D-S) belief networks for predicting fault-prone software modules. The novel approach demonstrates higher prediction accuracy compared to traditional methods on a NASA dataset.

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Last Updated: Apr 8, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Software Engineering
Artificial Intelligence
Data Mining

Background:

Identifying fault-prone modules is crucial for efficient software development and maintenance.
Traditional methods may have limitations in handling uncertainty and combining evidence.

Purpose of the Study:

To propose a novel methodology for predicting fault-prone software modules.
To leverage Dempster-Shafer (D-S) belief networks for enhanced fault prediction accuracy.

Main Methods:

Building a Dempster-Shafer network using an induction algorithm.
Selecting relevant predictors (attributes) via a logistic procedure.
Applying the trained D-S network to identify fault-prone modules using project-specific data.

Main Results:

The proposed methodology was applied to a NASA dataset.
Achieved higher prediction accuracy than logistic regression and discriminant analysis.
Demonstrated the effectiveness of D-S belief networks in fault prediction.

Conclusions:

The Dempster-Shafer belief network methodology offers a promising approach for predicting fault-prone software modules.
This method provides improved accuracy over existing techniques.
Highlights the potential of belief networks in software quality assurance.