Autofunk, a fast and scalable framework for building formal models from production systems

This paper proposes a model inference framework for production systems distributed over multiple devices exchanging thousands of events. Building models for such systems and keeping them up to date is time consuming and expensive, thus not adequately taken care of. Our framework, called Autofunk and designed with the collaboration of our industrial partner Michelin, combines formal model-driven engineering and expert systems to infer formal models that can be used to perform analyses, e.g. test case generation, or help diagnose faults in production by highlighting faulty behaviours. Given a large set of production events, we infer exact models that only capture the functional behaviours of a system under analysis. In this paper, we introduce and evaluate our framework on a real Michelin manufacturing system, showing that it can be used in practice.