Neural network application in support of software reliability engineering

Taghi M. Khoshgoftaar; David L. Lanning; Abhijit S. Pandya

doi:10.1117/12.205127

6 April 1995 Neural network application in support of software reliability engineering

Taghi M. Khoshgoftaar, David L. Lanning, Abhijit S. Pandya

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205127
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Taghi M. Khoshgoftaar, David L. Lanning, and Abhijit S. Pandya "Neural network application in support of software reliability engineering", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205127

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