Application of neural networks to software quality modeling of a very large telecommunications system
| Autor: | Edward B. Allen, Taghi M. Khoshgoftaar, S.J. Aud, J.P. Hudepohl |
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| Rok vydání: | 1997 |
| Předmět: |
Source lines of code
Computer Networks and Communications Computer science business.industry General Medicine Software prototyping Application software computer.software_genre Software metric Software quality Computer Science Applications Telecommunications control software Artificial Intelligence Software design business Telecommunications Risk assessment Software measurement computer Software Risk management |
| Zdroj: | IEEE Transactions on Neural Networks. 8:902-909 |
| ISSN: | 1941-0093 1045-9227 |
| DOI: | 10.1109/72.595888 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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