Active automata learning in practice: An annotated bibliography of the years 2011 to 2016

Abstract

Active automata learning is slowly becoming a standard tool in the toolbox of the software engineer. As systems become ever more complex and development becomes more distributed, inferred models of system behavior become an increasingly valuable asset for understanding and analyzing a system’s behavior. Five years ago (in 2011) we have surveyed the then current state of active automata learning research and applications of active automata learning in practice. We predicted four major topics to be addressed in the then near future: efficiency, expressivity of models, bridging the semantic gap between formal languages and analyzed components, and solutions to the inherent problem of incompleteness of active learning in black-box scenarios. In this paper we review the progress that has been made over the past five years, assess the status of active automata learning techniques with respect to applications in the field of software engineering, and present an updated agenda for future research.