Abstract: Testing has always been a challenge due to (1) its incompleteness by nature, (2) the lack of good specifications and (3) by its high demand for resources. With the growing complexity of the systems-under-test the situation is not likely to improve. The combination of model-learning with model-based testing offers an opportunity to master this complexity. In my talk I will introduce this line of research and report about our recent results including applications in the Internet of Things. We will cover the learning and testing of several classes of models/systems including Mealy machines, Markov decision processes and timed automata. Our goal is a natural evolution of testing: with the trend of our environment becoming "smarter", e.g. smart homes, smart cars, smart production, smart energy, our testing process needs to become smart as well. We are seeing the advent of smart testing.

CV: Bernhard K. Aichernig is a tenured associate professor at Graz University of Technology, Austria. He investigates the foundations of software engineering for realising dependable computer-based systems. Bernhard is an expert in formal methods and testing. His research covers a variety of areas combining falsification, verification and abstraction techniques. Current topics include the Internet of Things, model learning, and statistical model checking. Since 2006, he participated in four European projects. From 2004-2016 Bernhard served as a board member of Formal Methods Europe, the association that organises the Formal Methods symposia. From 2002 to 2006 he had a faculty position at the United Nations University in Macao S.A.R., China. Bernhard holds a habilitation in Practical Computer Science and Formal Methods, a doctorate, and a diploma engineer degree from Graz University of Technology.