On the neural network approach in software reliability modeling

DOI: Zhao Published 31 July Computer Science Journal of Convergence Information Technology During the last three decades, many software reliability models have been proposed and analyzed for measuring software reliability. Those models are mathematical models that represent software failures as a random process and can be used to evaluate development status during testing. Also they proposed artificial neural network based approach for software reliability estimation and modeling.

They already have shown how to apply neural network to predict a dynamic model. In this paper, we… Expand. View via Publisher. Save to Library Save. Create Alert Alert. Share This Paper. Methods Citations. Figures, Tables, and Topics from this paper. Artificial neural network Software reliability testing Mathematical model Data model List of software reliability models Stochastic process Software quality.

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. AB - 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.

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

More information Scopus Link. Procedia Computer Science, 57, — Google Scholar. Bisi, M. Benala, T. Behera, A. Lam, A. Nayak, S. Personalised recommendations. Cite paper How to cite? ENW EndNote. Buy options.