Improving Active Mealy Machine Learning for Protocol Conformance Testing

Abstract

Using a well-known industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R . Using active learning, we learn a model M _ R of reference implementation R , which serves as input for a model-based testing tool that checks conformance of implementation I to M _ R . In addition, we also explore an alternative approach in which we learn a model M _ I of implementation I , which is compared to model M _ R using an equivalence checker. Our work uses a unique combination of software tools for model construction (Uppaal), active learning (LearnLib, Tomte), model-based testing (JTorX, TorXakis) and verification (CADP, MRMC). We show how these tools can be used for learning models of and revealing errors in implementations, present the new notion of a conformance oracle, and demonstrate how conformance oracles can be used to speed up conformance checking.