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An S-shaped software reliability model with imperfect debugging and improved testing learning process

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  • Pratik Roy
  • G.S. Mahapatra
  • Kashi Nath Dey

Abstract

In this paper, we propose a non-homogeneous Poisson process (NHPP) based S-shaped software reliability growth model (SRGM) in presence of imperfect debugging with a new exponentially increasing fault content function and S-shaped fault detection rate. We develop the fault content function considering learning capability of testing team during software development process. Fault content increases rapidly at the beginning of testing process while it grows gradually at the end of testing process due to increasing efficiency of testing team with testing time. We use maximum likelihood estimation (MLE) method to estimate model parameters. Applicability of the proposed model has been presented by comparing with established models in terms of goodness of fit and predictive validity using two software failure data sets. Experimental results show that the proposed model gives better fit to real failure data sets and predicts future failure behaviour of software development accurately than established models.

Suggested Citation

  • Pratik Roy & G.S. Mahapatra & Kashi Nath Dey, 2013. "An S-shaped software reliability model with imperfect debugging and improved testing learning process," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 7(4), pages 372-387.
  • Handle: RePEc:ids:ijrsaf:v:7:y:2013:i:4:p:372-387
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    Cited by:

    1. Pooja Rani & GS Mahapatra, 2019. "A neuro-particle swarm optimization logistic model fitting algorithm for software reliability analysis," Journal of Risk and Reliability, , vol. 233(6), pages 958-971, December.

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