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A method of using Markov chain techniques for combinatorial test case selection is presented. The method can be used for statistical and coverage testing of many software programs, in particular for scientific computational software. The central point of the approach is modeling of dependencies between input parameters. Several different types of such dependencies are considered and models for each situation are created. Based on these models, test cases can be automatically generated and executed. Results of using the JUMBL tool for analyzing models and generating test cases are described.