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Application of Machine Learning Techniques to Predict Software Reliability

Author

Listed:
  • Ramakanta Mohanty

    (Berhampur University, India)

  • V. Ravi

    (Institute for Development and Research in Banking Technology, India)

  • M. R. Patra

    (Berhampur University, India)

Abstract

In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.

Suggested Citation

  • Ramakanta Mohanty & V. Ravi & M. R. Patra, 2010. "Application of Machine Learning Techniques to Predict Software Reliability," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 1(3), pages 70-86, July.
    • Handle: RePEc:igg:jaec00:v:1:y:2010:i:3:p:70-86
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    Cited by:

    1. Venkatesh, Kamini & Ravi, Vadlamani & Prinzie, Anita & Poel, Dirk Van den, 2014. "Cash demand forecasting in ATMs by clustering and neural networks," European Journal of Operational Research, Elsevier, vol. 232(2), pages 383-392.

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