Developing efficient and automatic testing techniques is one of the major challenges facing software validation community. In this paper, we show how a uniform random generation process of finite automata, developed in a recent work by Bassino and Nicaud, is relevant for many faces of automatic testing. The main contribution is to show how to combine two major testing approaches: model-based testing and random testing. This leads to a new testing technique successfully experimented on a realistic case study. We also illustrate how the power of random testing, applied on a Chinese Postman Problem implementation, points out an error in a well-known algorithm. Finally, we provide some statistics on model-based testing algorithms.