Process models tend to become more and more complex and, therefore, also more and more test cases are required to assure their correctness and stability during design and maintenance. However, executing hundreds or even thousands of process model test cases leads to excessive test suite execution times and, therefore, high costs. Hence, this paper presents a novel approach for process model test case selection which is able to address flexible user-driven test case selection requirements and which can integrate a diverse set of knowledge sources to select an appropriate minimal set of test cases which can be executed in minimal time. Additionally, techniques are proposed which enable the representation of unique coverage requirements and effects for each process node and process test case in a comprehensive way. For test case selection, a genetic algorithm is proposed. Its effectiveness is shown in comparison with other test case selection approaches.

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