Neural network application in support of software reliability engineering
Taghi M. Khoshgoftaar, David L. Lanning, Abhijit S. Pandya · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
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.