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Articles
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QICID: 24180
Title: An Integrated Model to Predict Fault Proneness Using Neural Networks
Copyright: ASQ
Author: Singh, Yogesh; Goel, Bindu
Organization: Guru Gobind Singh Indraprastha University
Subject: Software quality assurance (SQA), Reliability, Neural networks, Performance objectives, Prediction, Metrics, Principal components;
Series: Software Quality Professional, Vol. 10, No. 2, March 2008, pp. 22-32
This ARTICLE is available FREE
to all readers.
Abstract: [This abstract is based on the authors' abstract.]
The advantages attributed to object-oriented systems render software more manageable and less prone to error. Fault proneness can be estimated through certain measurable attributes if associations between the attributes and the system fault proneness can be established. This study performs an analysis of the integrated effect of the static measures and object-oriented metrics available on the fault proneness of the software. A model using artificial neural network modeling methods is demonstrated and validated for its ability to predict software reliability and the number of faults with as much as 76 percent accuracy.
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