title: Application of neural networks to software quality modeling of a very large telecommunications system.
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.
authors.affiliation: Dept. of Comput. Sci. and Eng., Florida Atlantic Univ., Boca Raton, FL.
authors.forename: T M
authors.initials: TM
authors.lastname: Khoshgoftaar
authors.forename: E B
authors.initials: EB
authors.lastname: Allen
authors.forename: J P
authors.initials: JP
authors.lastname: Hudepohl
authors.forename: S J
authors.initials: SJ
authors.lastname: Aud