Generating transition probabilities to support model-based software testing

Markov chain usage models support test planning, test automation, and analy sis of test results. In practice, transition probabilities for Markov chain usage models are often specified using a cycle of assigning, verifying, an d revising specific values for individual transition probabilities, For lar ge systems, such an approach can be difficult for a variety of reasons. We describe an improved approach that represents transition probabilities by e xplicitly preserving the information concerning test objectives and the rel ationships between transition probabilities in a format that is easy to mai ntain and easy to analyze. Using mathematical programming, transition proba bilities are automatically generated to satisfy test management objectives and constraints. A more mathematical treatment of this approach is given in References [1] (Poore JH, Walton GH, Whittaker JA, A constraint-based appr oach to the representation of software usage models, Information and Softwa re Technology 2000; at press) and [2] (Walton GH, Generating transition pro babilities for Markov chain usage models, PhD Thesis, University of Tenness ee, Knoxville, TN, May 1995.), In contrast, this paper is targeted at the s oftware engineering practitioner, software development manager, and test ma nager, This paper also adds to the published literature on Markov chain usa ge modeling and model-based testing by describing and illustrating an itera tive process for usage model development and optimization and by providing some recommendations for embedding model-based testing activities within an incremental development process. Copyright (C) 2000 John Wiley & Sons, Ltd .