Bayesian Algorithms for Evaluation and Prediction of Software Reliability
Park Man Gon · The Transactions of the Korea Information Processing Society · 1994
This paper proposes two Bayes estimators and their evaluation algorithms of the software reliability at the end testing stage in the Smith's Bayesian software reliability growth model under the data prior distribution BE(a, b), which is more general than uniform distribution, as a class of prior information. We consider both a squared-error loss function and the Harris loss function in the Bayesian estimation procedures. We also compare the MSE performances of the Bayes estimators and their algorithms of software reliability using computer simulations. And we conclude that the Bayes estimator of software reliability under the Harris loss function is more efficient than other estimators in terms of the MSE performances as a is larger and b is smaller, and that the Bayes estimators using the beta prior distribution as a conjugate prior is better than the Bayes estimators under the uniform prior distribution as a noninformative prior when a＞b.