Software metric-based neural network classification models of a very large telecommunications system

Publisher Website
Google Scholar
Abstract
Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Neural network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of unexpected problems with those modules. This paper is an experience report on a model of a large telecommunications system with almost 7,000 changed modules, consisting of over 7 million lines of code in procedures. We developed a neural network model to identify fault-prone modules based on nine design product metrics. Misclassification of not fault-prone modules would incur only modest cost in terms of extra attention to those modules. Misclassification of fault-prone modules would risk unexpected problems late in the development or even after release. Changed modules were randomly divided into a fit data set and a validate data set. We simulated utilization of the fitted model with the validate data set, successfully demonstrating generalization.

This publication has 0 references indexed in Scilit: