AI Planner Assisted Test Generation |
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Authors: | Amschler Andrews Anneliese K. Zhu Chunhui Scheetz Michael Dahlman Eric Howe Adele E. |
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Affiliation: | (1) School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163;(2) Computer Science Department, Colorado State University, Fort Collins, CO, 80523 |
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Abstract: | This paper describes an AI planner assisted approach to generate test cases for system testing based on high level test objectives. We use four levels of test generation: the metaprocessor, the preprocessor, the AI planner, and the postprocessor levels. Test generation is based on an extended UML model of the system under test and a mapping of high-level test objectives into initial and goal conditions of the planner. Test objectives are derived from a series of interviews with professional testers. We suggest various options for test criteria related to test objectives. The AI planner was used to generate hundreds of test cases for a robot controlled tape silo. The planner generated tests within a reasonable time. It was successful for each test objective given. |
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Keywords: | system test AI planning high level test objectives |
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