Application of neural networks to software quality modeling of a very large telecommunications system

  • Authors:

  • T. M. Khoshgoftaar;E. B. Allen;J. P. Hudepohl;S. J. Aud

  • Affiliations:

  • Dept. of Comput. Sci. & Eng., Florida Atlantic Univ., Boca Raton, FL;-;-;-

  • Venue:
  • IEEE Transactions on Neural Networks
  • Year:
  • 1997

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Abstract

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy