Improving Active Mealy Machine Learning for Protocol Conformance Testing

F. Aarts, H. Kuppens, G.J. Tretmans, F.W. Vaandrager, and S. Verwer, Improving Active Mealy Machine Learning for Protocol Conformance Testing. Machine Learning 96(1-2):189-224, July 2014. DOI: 10.1007/s10994-013-5405-0. This is a full version of our ICGI'12 paper.


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 MR of reference implementation R, which serves as input for a model based testing tool that checks conformance of implementation I to MR. In addition, we also explore an alternative approach in which we learn a model MI of implementation I, which is compared to model MR 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.

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