More and more applications rely on Machine Learning (ML) techniques, e.g., to automate software engineering. Like other applications, they need to be tested and validated. Testing ML-based software differs from testing software which do not rely on AI and ML: nondeterminism, lack of oracle, high dependence on training and evaluation data are hard points. The problem is even more complicated when humans are involved in the process. In this paper, we analyze the problem of evaluating OCE, a human-centered intelligent system based on reinforcement learning that automatically builds user-tailored software. We present a test environment composed of two tools which are based on the notions of "scenario" and "ideal assembly": OCE Scenario Maker to edit scenarios and OCE Scenario Runner to automate and repeat their execution.