Passive testing of production systems based on model inference

Abstract

This paper tackles the problem of testing produc­tion systems, i.e. systems that run in industrial environments, and that are distributed over several devices and sensors. Usually, such systems lack of models, or are expressed with models that are not up to date. Without any model, the testing process is often done by hand, and tends to be an heavy and tedious task. This paper contributes to this issue by proposing a framework called Autofunk, which combines different fields such as model inference, expert systems, and machine learning. This framework, designed with the collaboration of our industrial partner Michelin, infers formal models that can be used as specifications to perform offline passive testing. Given a large set of production messages, it infers exact models that only capture the functional behaviours of a system under analysis. Thereafter, inferred models are used as input by a passive tester, which checks whether a system under test conforms to these models. Since inferred models do not express all the possible behaviours that should happen, we define conformance with two implementation relations. We evaluate our framework on real production systems and show that it can be used in practice.