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Models -in particular finite state machine models -provide an invaluable source of information for the derivation of effective test cases. However, models usually approximate part of the program semantics and capture only some of the relevant dependencies and constraints. As a consequence, some of the test cases that are derived from models are infeasible.In this paper, we propose a method, based on the computation of the N-gram statistics, to increase the likelihood of deriving feasible test cases from a model. Correspondingly, the level of model coverage is also expected to increase, because infeasible test cases do not contribute to coverage. While N-grams do improve existing test case derivation methods, they show limitations when the N-gram statistics is incomplete, which is expected to necessarily occur as N increases. Interpolated N-grams overcome such limitation and show the highest performance of all test case derivation methods compared in this work.

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