Article ID: | iaor2003178 |
Country: | United States |
Volume: | 48 |
Issue: | 8 |
Start Page Number: | 747 |
End Page Number: | 763 |
Publication Date: | Dec 2001 |
Journal: | Naval Research Logistics |
Authors: | zekici Suleyman, Soyer R. |
Keywords: | markov processes |
We consider a software reliability model where the failure rate of each fault depends on the specific operation performed. The software is tested in a given sequence of test cases for fixed durations of time to collect data on failure times. We present a Bayesian analysis of software failure data by treating the initial number of faults as a random variable. Our analysis relies on the Markov Chain Monte Carlo methods and is used for developing optimal testing strategies in an adaptive manner. Two different models involving individual and common faults are analyzed. We illustrate an implementation of our approach by using some simulated failure data.