Model-Based Filtering of Combinatorial Test Suites

International audienceTobias is a combinatorial test generation tool which can efficiently generate a large number of test cases by unfolding a test pattern and computing all combinations of parameters. In this paper, we first propose a model-based testing approach where Tobias test cases are first run on an executable UML/OCL specification. This animation of test cases on a model allows to filter out invalid test sequences produced by blind enumeration, typically the ones which violate the pre-conditions of operations, and to provide an oracle for the valid ones. We then introduce recent extensions of the Tobias tool which support an incremental unfolding and filtering process, and its associated toolset. This allows to address explosive test patterns featuring a large number of invalid test cases, and only a small number of valid ones. For instance, these new constructs could mandate test cases to satisfy a given predicate at some point or to follow a given behavior. The early detection of invalid test cases improves the calculation time of the whole generation and execution process, and helps fighting combinatorial explosion

An Evaluation of Calibration Methods for Data Mining Models in Simulation Problems

Data mining is useful in making single decisions. The problem is when there are several related problems and the best local decisions do not make the best global result. We propose to calibrate each local data mining models in order to obtain accurate models, and to use simulation to merge the local models and obtain a good overall result.Bella Sanjuán, A. (2008). An Evaluation of Calibration Methods for Data Mining Models in Simulation Problems. http://hdl.handle.net/10251/13631Archivo delegad