Smyle: A Tool for Synthesizing Distributed Models from Scenarios by Learning

This paper presents Smyle, a tool for synthesizing asynchronous and distributed implementation models from sets of scenarios that are given as message sequence charts (MSCs). The latter specify desired or unwanted behavior of the system to be. Provided with such positive and negative example scenarios, Smyleemploys dedicated learning techniques and propositional dynamic logic(PDL) over MSCs to generate a system model that conforms with the given examples.

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