Smyle: A Tool for Synthesizing Distributed Models from Scenarios by Learning
Benedikt Bollig, Joost-Pieter Katoen, Carsten Kern, Martin Leucker · 2008
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, Smyle employs dedicated learning techniques and propositional dynamic logic (PDL) over MSCs to generate a system model that conforms with the given examples. Synthesizing distributed systems from user-specified scenarios is becoming increasingly en vogue [7]. There exists a wide range of approaches for synthesizing implementation models from a priori given scenarios [6,11,8,5, 14,15]. The approaches mainly differ in their specification language, the inference procedure, and the final implementation model. Several of them employ MSCs as specification language because they are standardized by the ITU Z.120 [9] and adopted by the UML as sequence diagrams. Other approaches try to utilize more expressive notations like triggered MSCs [14], high-level MSCs [6], or live sequence charts [8]. On the one hand, more expressive power results in richer specifications. On