MOST: A Multi-objective Search-Based Testing from EFSM

This paper introduces a multi-objective evolutionary approach to test case generation from extended finite state machines (EFSM), named MOST. Testing from an (E)FSM generally involves executing various transition paths, until a given coverage criterion (e.g. cover all transitions) is met. As traditional test generation methods from FSM only consider the control aspects, they can produce many infeasible paths when applied to EFSMs, due to conflicts in guard conditions along a path. In order to avoid the infeasible path generation, we propose an approach that obtains feasible paths dynamically, instead of performing static reach ability analysis as usual for FSM-based methods. Previous works have treated EFSM test case generation as a mono-objective optimization problem. Our approach takes two objectives into account that are the coverage criterion and the solution length. In this way, it is not necessary to establish in advance the test case size as earlier approaches. MOST constructs a Pareto set approximation, i.e., a group of optimal solutions, which allows the test team to select the solutions that represent a good trade-off between both objectives. The paper shows empirical studies to illustrate the benefits of the approach and comparing the results with the ones obtained in a related work.