Generating Feasible Test Paths from an Executable Model Using a Multi-objective Approach

Abstract

Search-based testing techniques using meta-heuristics, like evolutionary algorithms, has been largely used for test data generation, but most approaches were proposed for white-box testing. In this paper we present an evolutionary approach for test sequence generation from a behavior model, in particular, Extended Finite State Machine. An open problem is the production of infeasible paths, as these should be detected and discarded manually. To circumvent this problem, we use an executable model to obtain feasible paths dynamically. An evolutionary algorithm is used to search for solutions that cover a given test purpose, which is a transition of interest. The target transition is used as a criterion to get slicing information, in this way, helping to identify the parts of the model that affect the test purpose. We also present a multi-objective search: the test purpose coverage and the sequence size minimization, as longer sequences require more effort to be executed.