Abstract
Software testing is an expensive, yet essential stage in all software development models, thus there is a great effort from the research community to facilitate or even automate this step. Although much of the testing process is automated by modern software development environments (e.g., test execution, monitoring), the selection of test data remains generally a manual process. In this paper we present a novel approach for test data generation in case of testing data dependent behaviour of autonomous software agents. The proposed method uses the metamodel of the agent’s environment derived from the context ontology, and utilizes the input specifications to formulate the goal of testing. Our approach suggests the use of metaheuristic search techniques for the generation of optimal test data, usually referred to as search-based software test data generation.
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