TTCN-3 Test Data Analyser Using Constraint Programming

This paper presents the idea of analyzing and refining the test data variance computation as a method to determine the quality of TTCN-3 tests. TTCN-3 as the only internationally standardized testing language is being used extensively by industry. A large amount of functional test specifications are written in this language. For this reason, a sensible issue is the quality analysis of these specifications. Data variance characterizes the test data distribution over the test system interface. TTCN-3 template distance constitutes the criterion to quantify the variance: "similar"and "different enough" test data. Due to the high flexibility of the language, the stimuli templates to be compared may not only contain concret evalues but also variables, function calls, parameters, values which are known at runtime only, etc. Therefore, we combine constraint programming (CP) with static analysis of TTCN-3 test suites targeting a realistic template solving. We name template constraint an expression whose value (or domain restrictions) can only be determined by looking at the execution paths in the analyzed test behavior. This method leads to a better computation of the data distance and, thus, to a better refinement of similarity classes. The approach is illustrated by an example which shows how data varies when the distance is computed taking into account the template constraints.