Automata Learning for Dynamic Software Product Lines: A Tutorial

Abstract

Automata learning is a fundamental techique for building behavioral models by actively interacting with black-box systems. Through four decades of research the community has come up with many algorithms and tools for automata learning, of which this tutorial will provide an overview. Moreover, researchers have proposed several extensions of automata learning algorithms to evolving systems, i.e., systems that change in time, as well as variability-intensive systems, i.e., systems that change in configuration space. In this tutorial, we provide an overview of such extensions and show how they can be applied to the field of dynamic software product lines.