Bayesian Algorithms for Evaluation and Prediction of Software Reliability


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 1, No. 1, pp. 14-22, May. 1994
10.3745/KIPSTE.1994.1.1.14,   PDF Download:

Abstract

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 beta 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 under the Harris loss functions 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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
P. M. Gon, "Bayesian Algorithms for Evaluation and Prediction of Software Reliability," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 1, no. 1, pp. 14-22, 1994. DOI: 10.3745/KIPSTE.1994.1.1.14.

[ACM Style]
Park Man Gon. 1994. Bayesian Algorithms for Evaluation and Prediction of Software Reliability. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 1, 1, (1994), 14-22. DOI: 10.3745/KIPSTE.1994.1.1.14.