Cookies

We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Department of Mathematical Sciences

Staff

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

Abstract

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.

References

J.M. Bernardo and A.F.M. Smith. Bayesian Theory, Wiley, Chichester (1994).
2. R.M. Cooke. Experts in Uncertainty, Oxford University Press, New York (1991).
3. P.S. Craig, M. Goldstein, A.H. Seheult and J.A. Smith, Pressure matching for hydrocarbon reservoirs: a case study in the use of Bayes linear strategies for large computer experiments (with discussion). In: C.
Gatsonis, J.S. Hodges, R.E. Kass, R. McCulloch, P. Rossi and N.D.
Singpurwalla, Editors, Case Studies in Bayesian Statistics, Springer, New York (1997), pp. 37–93.
4. P.S. Craig, M. Goldstein, A.H. Seheult and J.A. Smith, Constructing partial prior specifications for models of complex physical systems. The Statistician 47 (1998), pp. 37–53 5. B. De Finetti. Theory of Probability, Wiley, Chichester (1974) 2 volumes.
6. M. Farrow and M. Goldstein, Reconciling costs and benefits in experimental design. In: J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M.
Smith, Editors, Bayesian Statistics 4, Oxford University Press, New York (1992), pp. 607–615.
7. M. Farrow and M. Goldstein, Bayes linear methods for grouped multivariate repeated measurement studies with application to crossover trials. Biometrika 80 (1993), pp. 39–59 8. M. Farrow, M. Goldstein and T. Spiropoulos, Developing a Bayes linear decision support system for a brewery. In: S. French and J.Q. Smith, Editors, The Practice of Bayesian Analysis, Edward Arnold (1997), pp.
71–106.
9. A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin. Bayesian Data Analysis, Chapman & Hall, London (1995).
10. M. Goldstein, Exchangeable belief structures. Journal of the American Statistical Association 81 (1986), pp. 971–976.
11. M. Goldstein, Revising exchangeable beliefs: subjectivist foundations for the inductive argument. In: P.R. Freeman and A.F.M. Smith, Editors, Aspects of Uncertainty, Wiley, Chichester (1994), pp. 201–222.
12. M. Goldstein, Prior inferences for posterior judgements. In: M.L.D.
Chiara, Editor, Proceedings of the 10th International Congress of Logic, Methodology and Philosophy of Science, Kluwer, Dordrecht (1996).
13. M. Goldstein, Bayes linear analysis. In: S. Kotz, C.B. Read and D.L.
Banks, Editors, Encyclopaedia of Statistical Sciences, Update vol. 3, Wiley, New York (1999), pp. 29–34.
14. M. Goldstein and D.A. Wooff, Bayes linear computation: concepts, implementation and programs. Statistics and Computing 5 (1995), pp.
327–341.
15. M. Goldstein and D.A. Wooff, Choosing sample sizes in balanced experimental designs: a Bayes linear approach. The Statistician 46 (1997), pp. 167–183.
16. D. Hamlet, Are we testing for true reliability. IEEE Software July (1992), pp. 21–27.
17. D. Hamlet and R. Taylor, Partition testing does not inspire confidence. IEEE Transactions on Software Engineering 16 (1990), pp.
1402–1411.
18. S.L. Lauritzen. Graphical Models, Oxford University Press, New York (1996).
19. G. Myers. The Art of Software Testing, Wiley, New York (1979).
20. R.M. Oliver and J.Q. Smith, Editors, Influence Diagrams, Belief Nets and Decision Analysis, Wiley, London (1990).
21. P. Stevens and R. Pooley. Using UML; Software Engineering with Objects and Components, Addison-Wesley, Harlow, UK (2000) updated version.
22. E.J. Weyuker and B. Jeng, Analyzing partition testing strategies. IEEE Transactions on Software Engineering 17 (1991), pp. 703–711 23. E.J. Weyuker and T.J. Ostrand, Theories of program testing and the application of revealing subdomains. IEEE Transactions on Software Engineering 6 (1980), pp. 236–246.