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Bayesian graphical models for high complexity testing: aspects of implementation

Wooff, D.A.; Goldstein, M.; Coolen, F.P.A.

Authors

D.A. Wooff

M. Goldstein



Contributors

R.S. Kenett
Editor

F. Ruggeri
Editor

F.W. Faltin
Editor

Abstract

This chapter presents a brief review of the Bayesian graphical models (BGM) approach to software testing, which the authors developed in close collaboration with industrial software testers. It provides discussion of a range of topics for practical implementation of the BGM approach, including modeling for test–retest scenarios, the expected duration of the retest cycle, incorporation of multiple failure modes, and diagnostic methods. The chapter addresses model maintenance and evolution, including consideration of novel system functionality. It discusses end‐to‐end testing of complex systems, and presents methods to assess the viability of the BGM approach for individual applications. These are all important aspects of high‐complexity testing which software testers have to deal with in practice, and for which Bayesian statistical methods can provide useful tools. The chapter also provides the basic approaches to these important issues. These should enable software testers, with support from statisticians, to develop implementations for specific test scenarios.

Citation

Wooff, D., Goldstein, M., & Coolen, F. (2018). Bayesian graphical models for high complexity testing: aspects of implementation. In R. Kenett, F. Ruggeri, & F. Faltin (Eds.), Analytic methods in systems and software testing (213-243). Wiley. https://doi.org/10.1002/9781119357056.ch8

Online Publication Date Jul 6, 2018
Publication Date Jul 6, 2018
Deposit Date Jul 24, 2018
Publisher Wiley
Pages 213-243
Book Title Analytic methods in systems and software testing.
Chapter Number 8
DOI https://doi.org/10.1002/9781119357056.ch8


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