Abstract—Black-box test-generation requires a model of the
system under test to describe 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-generation in which an AI
(artificial intelligence) planner is used to generate test cases from
test objectives derived from UML (Unified Modeling Language)
Class Diagrams. The UML Class Diagrams are conceptual models
of the systems under test. They differ from traditional design and
requirements models in that they include information pertinent
to test case generation. From these 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 satisfies the UML-derived test objectives. This paper describes
the application of the testing approach to an industrial problem.
Index Terms—AI (artificial intelligence) planning, test-case generation,
test objective, UML (unified modeling language) class diagram
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