Martin Atzmueller, Joachim Baumeister, Frank Puppe
In this paper we present a novel approach for pattern-constrained test case generation. The generation of test cases with known characteristics is usually a non-trivial task. In contrast, the proposed method allows for a transparent and intuitive modeling of the relations contained in the test data. For the presented approach, we utilize a general-purpose data generator: It relies on easy to understand subgroup patterns which are mapped to a Bayesian network representing the data generation model. The data generation phase is embedded into an incremental process for quality control and adaptation of the generated test cases. We provide a case study in the biological domain exemplifying the presented approach.
Subjects: 10. Knowledge Acquisition; 12. Machine Learning and Discovery
Submitted: Feb 9, 2007