22 March 1996 Software metric-based neural network classification models of a very large telecommunications system
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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.
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Taghi M. Khoshgoftaar, Edward B. Allen, John Hudepohl, Steve Aud, "Software metric-based neural network classification models of a very large telecommunications system", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235967; https://doi.org/10.1117/12.235967
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