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Bayesian testing strategies for software with an operational profile
Author(s) -
Özekici S.,
Soyer R.
Publication year - 2001
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.1046
Subject(s) - computer science , markov chain monte carlo , software , bayesian probability , reliability engineering , software quality , markov chain , reliability (semiconductor) , failure rate , data mining , software development , artificial intelligence , machine learning , engineering , power (physics) , physics , quantum mechanics , programming language
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. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:747–763, 2001