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AI Planner Assisted Test Generation
Authors:Amschler Andrews  Anneliese K.  Zhu  Chunhui  Scheetz  Michael  Dahlman  Eric  Howe  Adele E.
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
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.
Keywords:system test  AI planning  high level test objectives
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