Testing analog, mixed-signal and RF (AMS-RF) cir- cuits represents a significant cost component for testing complex SoCs. Moreover, AMS-RF test generation and validation are still largely handcrafted tasks that rely on expert design knowledge for each particular Device Under Test (DUT). Mixed-signal test automation has been sought by the test community for the last decades, trying to mimic the success of digital test approaches. Indeed, in the digital domain, test is vastly automated and standard techniques are already available (ATPGs, BIST, scan registers, etc.). In the last decade, a methodology based on leveraging the power of machine learning algorithms has been proposed for AMS-RF circuits that opens the door to a higher level of automation. In this paper we review recent results in this line and try to put together what could be such a complete methodology and what remains to be done.