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An Empirical Analysis of Software Reliability Prediction Through Reliability Growth Model Using Computational Intelligence

  • Manmath Kumar Bhuyan
  • Durga Prasad Mohapatra
  • Srinivas Sethi
  • Sumit Kar
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

The objective of this paper is to predict software reliability using non-parametric neural network of computational intelligence (CI). The study uses data sets containing failure history such as number of failures, failure time interval etc. In this paper, we explore the applicability of feed-forward neural network with back-propagation training as a reliability growth model for software reliability prediction. The prediction result is compared with that of traditional parametric software reliability growth models. The results described in the proposed model exhibits an accurate and consistent behavior in reliability prediction. The experimental results demonstrate that the proposed model provides a significant difference respect to accuracy and consistency.

Keywords

Software reliability prediction Reliability growth models Neural networks 

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Copyright information

© Springer India 2015

Authors and Affiliations

  • Manmath Kumar Bhuyan
    • 1
  • Durga Prasad Mohapatra
    • 2
  • Srinivas Sethi
    • 1
  • Sumit Kar
    • 1
  1. 1.Department of Computer Science and EngineeringIGIT, Utkal UniversityBhubaneswarIndia
  2. 2.Department of Computer Science and EngineeringNITRourkelaIndia

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