Learning regular sets from queries and counterexamples
Information and Computation
MAS — an interactive synthesizer to support behavioral modelling in UML
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Come, Let's Play: Scenario-Based Programming Using LSC's and the Play-Engine
Come, Let's Play: Scenario-Based Programming Using LSC's and the Play-Engine
Generating Annotated Behavior Models from End-User Scenarios
IEEE Transactions on Software Engineering
Infinite-state high-level MSCs: Model-checking and realizability
Journal of Computer and System Sciences
Triggered Message Sequence Charts
IEEE Transactions on Software Engineering
Behaviour Model Synthesis from Properties and Scenarios
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Replaying play in and play out: synthesis of design models from scenarios by learning
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
Propositional dynamic logic for message-passing systems
FSTTCS'07 Proceedings of the 27th international conference on Foundations of software technology and theoretical computer science
LearnLib: a library for automata learning and experimentation
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
The practical assessment of test sets with inductive inference techniques
TAIC PART'10 Proceedings of the 5th international academic and industrial conference on Testing - practice and research techniques
Increasing functional coverage by inductive testing: a case study
ICTSS'10 Proceedings of the 22nd IFIP WG 6.1 international conference on Testing software and systems
From ZULU to RERS: lessons learned in the ZULU challenge
ISoLA'10 Proceedings of the 4th international conference on Leveraging applications of formal methods, verification, and validation - Volume Part I
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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.