Applications of Spiking Neural Network to Predict Software Reliability

  • Ramakanta Mohanty
  • Aishwarya Priyadarshini
  • Vishnu Sai Desai
  • G. Sirisha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


In the period of software improvement, programming dependability expectation turned out to be exceptionally critical for creating nature of programming in the product business. Time to time, numerous product dependability models have been introduced for evaluating unwavering quality of programming in programming forecast models. However, building precise forecast model is hard because of intermittent changes in information in the space of programming designing. As needs be, we propose a novel procedure, i.e. spiking neural system to anticipate programming unwavering quality. The key goal of this paper is to exhibit another approach which upgrades the exactness of programming unwavering quality prescient models when utilized with the product disappointment dataset. The viability of quality of a product is exhibited on dataset taken from the literature, where execution is measured by utilizing normalized root mean square error (NRMSE) obtained in the test dataset.


Software reliability Neural network Spiking neural network Normalized root mean square error 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ramakanta Mohanty
    • 1
  • Aishwarya Priyadarshini
    • 2
  • Vishnu Sai Desai
    • 1
  • G. Sirisha
    • 1
  1. 1.Department of Computer Science & EngineeringKeshav Memorial Institute of TechnologyHyderabadIndia
  2. 2.Department of Computer Science and EngineeringInternational Institute of Information TechnologyBhubaneswarIndia

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