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Improving active Mealy machine learning for protocol conformance testing
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DOI：10.1007/s10994-013-5405-0.png)
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Abstract
En 中文
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.
Keywords:
Active learning
Automaton learning
Mealy machines
State machine synthesis
Model-based testing
Protocol learning
Model checking