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Proceedings Paper

Neural network application in support of software reliability engineering
Author(s): Taghi M. Khoshgoftaar; David L. Lanning; Abhijit S. Pandya
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Paper Abstract

This paper presents a novel application of neural networks to the problem of classifying software modules into different risk classes based upon source code measures. Neural network models that classify program modules as either high-risk or low-risk are developed. Inputs to these networks include a selection of source code measure data and fault data that were collected from two large commercial systems. The criterion variable for class determination was a quality measure of program faults or changes. Discriminant models using the same data sets provide for a comparative analysis. The neural network technique displayed better classification error rates on both data sets. These successes demonstrate the utility of neural networks in isolating high-risk modules.

Paper Details

Date Published: 6 April 1995
PDF: 8 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205127
Show Author Affiliations
Taghi M. Khoshgoftaar, Florida Atlantic Univ. (United States)
David L. Lanning, IBM Corp. (United States)
Abhijit S. Pandya, Florida Atlantic Univ. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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