On the neural network approach in software reliability modeling

Kai Yuan Cai, Lin Cai, Wei Dong Wang, Zhou Yi Yu, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

125 Citations (Scopus)


Previous studies have shown that the neural network approach can be applied to identify defect-prone modules and predict the cumulative number of observed software failures. In this study we examine the effectiveness of the neural network approach in handling dynamic software reliability data overall and present several new findings. Specifically, we find 1. The neural network approach is more appropriate for handling datasets with 'smooth' trends than for handling datasets with large fluctuations. 2. The training results are much better than the prediction results in general. 3. The empirical probability density distribution of predicting data resembles that of training data. A neural network can qualitatively predict what it has learned. 4. Due to the essential problems associated with the neural network approach and software reliability data, more often than not, the neural network approach fails to generate satisfactory quantitative results.
Original languageEnglish
Pages (from-to)47-62
Number of pages16
JournalJournal of Systems and Software
Issue number1
Publication statusPublished - 15 Aug 2001


  • Empirical probability density distribution
  • Filtering
  • Network architecture
  • Neural network
  • Scaling function
  • Software operational profile
  • Software reliability modeling

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture

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