Generating consistent test data: Restricting the search space by a generator formula

To address the problem of generating test data for a set of general consistency constraints, we propose a new two-step approach: First the interdepen-dencies between consistency constraints are explored and a generator formula is derived on their basis. During its creation, the user may exert control. In essence, the generator formula contains information to restrict the search for consistent test databases. In the second step, the test database is generated. Here, two different approaches are proposed. The first adapts an already published approach to generating finite models by enhancing it with requirements imposed by test data generation. The second, a new approach, operationalizes the generator formula by translating it into a sequence of operators, and then executes it to construct the test database. For this purpose, we introduce two powerful operators: the generation operator and the test-and-repair operator. This approach also allows for enhancing the generation operators with heuristics for generating facts in a goal-directed fashion. It avoids the generation of test data that may contradict the consistency constraints, and limits the search space for the test data. This article concludes with a careful evaluation and comparison of the performance of the two approaches and their variants by describing a number of benchmarks and their results.