Generating test-cases from an object-oriented model with an artificial-intelligence planning system

Black-box test-generation requires a model of the system under test to desc ribe what is to be tested. Testing criteria and test objectives define how it is to be tested. This paper describes an approach to black-box test-gene ration in which an AI (artificial intelligence) planner is used to generate test cases from test objectives derived from UML (Unified Modeling Languag e) Class Diagrams. The UML Class Diagrams are conceptual models of the syst ems under test. They differ from traditional design and requirements models in that they include information pertinent to test case generation. From t hese models, test objectives and a domain theory are: obtained, transformed to planner representations, and input to the planner. The planner uses the problem description to generate a test suite that sati sfies the UML-derived test objectives. This paper describes the application of the testing approach to an industrial problem.