Robust recurrent neural network modeling for software fault detection and correction prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

124 Scopus Citations
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  • Q. P. Hu
  • M. Xie
  • S. H. Ng
  • G. Levitin


Original languageEnglish
Pages (from-to)332-340
Journal / PublicationReliability Engineering and System Safety
Issue number3
Publication statusPublished - Mar 2007
Externally publishedYes


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.

Research Area(s)

  • Artificial neural networks, Reliability prediction, Software fault correction, Software fault detection, Software reliability growth model