  Search-based testing is widely used to find bugs in models of complex
Cyber-Physical Systems. Latest research efforts have improved this approach by
casting it as a falsification procedure of formally specified temporal
properties, exploiting the robustness semantics of Signal Temporal Logic. The
scaling of this approach to highly complex engineering systems requires
efficient falsification procedures, which should be applicable also to black
box models. Falsification is also exacerbated by the fact that inputs are often
time-dependent functions. We tackle the falsification of formal properties of
complex black box models of Cyber-Physical Systems, leveraging machine learning
techniques from the area of Active Learning. Tailoring these techniques to the
falsification problem with time-dependent, functional inputs, we show a
considerable gain in computational effort, by reducing the number of model
simulations needed. The goodness of the proposed approach is discussed on a
challenging industrial-level benchmark from automotive.
