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Durham University

Department of Mathematical Sciences


Publication details for Michael Goldstein

Coolen, F. P. A., Goldstein M. & Munro, M. (2001). Generalized partition testing via Bayes linear methods. Information and software technology 43(13): 783-793.

Author(s) from Durham


This paper explores the use of Bayes linear methods related to partition testing for software. If a partition of the input domain has been defined, the method works without the assumption of homogeneous (revealing) subdomains, and also includes the possibility to learn, from testing inputs in one subdomain, about inputs in other subdomains, through explicit definition of the correlations involved. To enable practical application, an exchangeability structure needs to be defined carefully, for which means the judgements of experts with relation to the software is needed. Next to presenting the basic idea of Bayes linear methods and how it can be used to generalize partition testing, some important aspects related to applications as well as for future research are discussed.


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