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A Study of a Single Multiplicative Neuron (SMN) Model for Software Reliability Prediction

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Innovations in Intelligent Machines -3

Part of the book series: Studies in Computational Intelligence ((SCI,volume 442))

Abstract

This chapter presents a single multiplicative neuron model for predicting software failure has been proposed. Standard back propagation and real coded genetic algorithm with mean squarer error as a fitness function are used for optimizing the parameters. The performance of the proposed model is validated using some real software failure data. A comparative study between some existing software reliability models and the proposed model is presented using different comparison criteria.

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Author information

Authors and Affiliations

  1. Dept. of Applied Mathematics, ISM, Dhanbad, 826004, India

    S. Chatterjee, J. B. Singh, S. Nigam & L. N. Upadhyaya

Authors
  1. S. Chatterjee
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  2. J. B. Singh
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  3. S. Nigam
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  4. L. N. Upadhyaya
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Corresponding author

Correspondence to S. Chatterjee .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, PO1 3HE, Portsmouth, United Kingdom

    Ivan Jordanov

  2. School of Electrical and Information Eng, University of South Australia, Adelaide, SA 5095, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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Chatterjee, S., Singh, J.B., Nigam, S., Upadhyaya, L.N. (2013). A Study of a Single Multiplicative Neuron (SMN) Model for Software Reliability Prediction. In: Jordanov, I., Jain, L.C. (eds) Innovations in Intelligent Machines -3. Studies in Computational Intelligence, vol 442. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32177-1_6

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  • DOI: https://doi.org/10.1007/978-3-642-32177-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32176-4

  • Online ISBN: 978-3-642-32177-1

  • eBook Packages: EngineeringEngineering (R0)

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