Please use this identifier to cite or link to this item:
Title: Robust recurrent neural network modeling for software fault detection and correction prediction
Authors: Hu, Q.P.
Xie, M. 
Ng, S.H. 
Levitin, G.
Keywords: Artificial neural networks
Reliability prediction
Software fault correction
Software fault detection
Software reliability growth model
Issue Date: Mar-2007
Citation: Hu, Q.P., Xie, M., Ng, S.H., Levitin, G. (2007-03). Robust recurrent neural network modeling for software fault detection and correction prediction. Reliability Engineering and System Safety 92 (3) : 332-340. ScholarBank@NUS Repository.
Abstract: Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set. © 2006 Elsevier Ltd. All rights reserved.
Source Title: Reliability Engineering and System Safety
ISSN: 09518320
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.