Analysis of Software Reliability Data using Exponential Power Model
V. Ashwini Kumar Srivastava. International Journal of Advanced Computer Science and Applications(IJACSA), (2011)
Abstract
In this paper, Exponential Power (EP) model is proposed to analyze the software reliability data and the present work is an attempt to represent that the model is as software reliability model. The approximate MLE using Artificial Neural Network (ANN) method and the Markov chain Monte Carlo (MCMC) methods are used to estimate the parameters of the EP model. A procedure is developed to estimate the parameters of the EP model using MCMC simulation method in OpenBUGS by incorporating a module into OpenBUGS. The R functions are developed to study the various statistical properties of the proposed model and the output analysis of MCMC samples generated from OpenBUGS. A real software reliability data set is considered for illustration of the proposed methodology under informative set of priors.
%0 Journal Article
%1 IJACSA.2011.020208
%A Ashwini Kumar Srivastava, Vijay Kumar
%D 2011
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K Bayesian Cumulative Hazard MLE, Parameter Probability Reliability density density, estimation, estimation., function, function,EP model rate
%N 2
%T Analysis of Software Reliability Data using Exponential Power Model
%U http://ijacsa.thesai.org/
%V 2
%X In this paper, Exponential Power (EP) model is proposed to analyze the software reliability data and the present work is an attempt to represent that the model is as software reliability model. The approximate MLE using Artificial Neural Network (ANN) method and the Markov chain Monte Carlo (MCMC) methods are used to estimate the parameters of the EP model. A procedure is developed to estimate the parameters of the EP model using MCMC simulation method in OpenBUGS by incorporating a module into OpenBUGS. The R functions are developed to study the various statistical properties of the proposed model and the output analysis of MCMC samples generated from OpenBUGS. A real software reliability data set is considered for illustration of the proposed methodology under informative set of priors.
@article{IJACSA.2011.020208,
abstract = {In this paper, Exponential Power (EP) model is proposed to analyze the software reliability data and the present work is an attempt to represent that the model is as software reliability model. The approximate MLE using Artificial Neural Network (ANN) method and the Markov chain Monte Carlo (MCMC) methods are used to estimate the parameters of the EP model. A procedure is developed to estimate the parameters of the EP model using MCMC simulation method in OpenBUGS by incorporating a module into OpenBUGS. The R functions are developed to study the various statistical properties of the proposed model and the output analysis of MCMC samples generated from OpenBUGS. A real software reliability data set is considered for illustration of the proposed methodology under informative set of priors.
},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Ashwini Kumar Srivastava}, Vijay Kumar},
biburl = {https://www.bibsonomy.org/bibtex/2e6205b97fa2fd46388d0e3b1782fc4d6/thesaiorg},
interhash = {c0e1cf15dd934795c9131da0aff75b31},
intrahash = {e6205b97fa2fd46388d0e3b1782fc4d6},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {Bayesian Cumulative Hazard MLE, Parameter Probability Reliability density density, estimation, estimation., function, function,EP model rate},
number = 2,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Analysis of Software Reliability Data using Exponential Power Model}},
url = {http://ijacsa.thesai.org/},
volume = 2,
year = 2011
}