Analysis of Gumbel Model for Software Reliability Using Bayesian Paradigm

In this paper, we have illustrated the suitability of Gumbel Model for software reliability data. The model parameters are estimated using likelihood based inferential procedure: classical as well as Bayesian. The quasi Newton-Raphson algorithm is applied to obtain the maximum likelihood estimates and associated probability intervals. The Bayesian estimates of the parameters of Gumbel model are obtained using Markov Chain Monte Carlo(MCMC) simulation method in OpenBUGS(established software for Bayesian analysis using Markov Chain Monte Carlo methods). The R functions are developed to study the statistical properties, model validation and comparison tools of the model and the output analysis of MCMC samples generated from OpenBUGS. Details of applying MCMC to parameter estimation for the Gumbel model are elaborated and a real software reliability data set is considered to illustrate the methods of inference discussed in this paper.

@article{Kumar2012,
  title     = {Analysis of Gumbel Model for Software Reliability Using Bayesian Paradigm},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {1},
  number    = {9},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {Raj Kumar and Ashwini Kumar Srivastava and Vijay Kumar},
  doi       = {10.14569/IJARAI.2012.010907},
  url       = {https://doi.org/10.14569/IJARAI.2012.010907}
}