In evolutionary robotics, controllers are often designed in simulation, then transferred onto the real system. Nevertheless, when no accurate model is available, controller transfer from simulation to reality means potential performance loss. It is the reality gap problem. Unmanned aerial vehicles are typical systems where it may arise. Their locomotion dynamics may be hard to model because of a limited knowledge about the underlying physics. Moreover, a batch identification approach is difficult to use due to costly and time consuming experiments. An automatic identification method is then needed that builds a relevant local model of the system concerning a target issue. This paper deals with such an approach that is based on coevolution of models and tests. It aims at improving both modeling and control of a given system with a limited number of manipulations carried out on it. Experiments conducted with a simulated quadrotor helicopter show promising initial results about test learning and control improvement.