This paper presents a method for test case selection that allows a formal approach to testing software. The two main ideas are (1) that testers create stochastic models of software behavior instead of crafting individual test cases and (2) that specific test cases are generated from the stochastic models and applied to the software under test. This paper describes a method for creating a stochastic model in the context of a solved example. We concentrate on Markov models and show how non‐Markovian behavior can be embedded in such models without violating the Markov property.
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