Learning Communicating Automata from MSCs

This paper is concerned with bridging the gap between requirements and distributed systems. Requirements are defined as basic message sequence charts (MSCs) specifying positive and negative scenarios. Communicating finite-state machines (CFMs), i.e., finite automata that communicate via FIFO buffers...

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Veröffentlicht in:IEEE transactions on software engineering Jg. 36; H. 3; S. 390 - 408
Hauptverfasser: Bollig, Benedikt, Katoen, Joost-Pieter, Kern, Carsten, Leucker, Martin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2010
IEEE Computer Society
Institute of Electrical and Electronics Engineers
Schlagworte:
Algorithms
Artificial intelligence
Automation
Buffers
Communicating
Communication channels
Communications networks
Computer Science
Computer Society
computing methodologies/artificial intelligence/learning/induction
Design engineering
Design methodology
Digital Object Identifier
Feasibility
Learning
Learning automata
Mathematical analysis
Messages
Optimization
Optimization techniques
Other
Requirements
Software design
Software engineering
software engineering/design/design concepts
Software engineering/requirements/specifications/elicitation methods
Studies
System recovery
theory of computation/computation by abstract devices/models of computation/automata
Unified modeling language
ISSN:0098-5589, 1939-3520
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Beschreibung
Zusammenfassung:This paper is concerned with bridging the gap between requirements and distributed systems. Requirements are defined as basic message sequence charts (MSCs) specifying positive and negative scenarios. Communicating finite-state machines (CFMs), i.e., finite automata that communicate via FIFO buffers, act as system realizations. The key contribution is a generalization of Angluin's learning algorithm for synthesizing CFMs from MSCs. This approach is exact-the resulting CFM precisely accepts the set of positive scenarios and rejects all negative ones-and yields fully asynchronous implementations. The paper investigates for which classes of MSC languages CFMs can be learned, presents an optimization technique for learning partial orders, and provides substantial empirical evidence indicating the practical feasibility of the approach.
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ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2009.89