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Electronic Notes in Theoretical Computer Science
Article . 2016 . Peer-reviewed
License: CC BY NC ND
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Electronic Notes in Theoretical Computer Science
Article
License: CC BY NC ND
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Translating Controlled Graph Grammars to Ordinary Graph Grammars

Authors: Alex Bertei; Luciana Foss; Simone André da Costa Cavalheiro;

Translating Controlled Graph Grammars to Ordinary Graph Grammars

Abstract

AbstractGraph Grammar (GG) is an appropriate formal language for specifying complex systems. In a GG the system states are represented by graphs and the changes between the states are described by rules. The use of GGs is interesting as there are several techniques for the specification and verification of systems that are described in this language. Besides this, GGs have a graphical representation which is quite intuitive, making the language easy to understand even for non-theorists. Controlled graph grammars (CGGs) are GGs that allow defining a sequence of rule applications, considering an auxiliary control structure, allowing one to control the intrinsic non-determinism of this formalism. However, for this type of grammar there are no tools for verification of properties. Therefore, the aim of this paper is to translate the controlled graph grammars into ordinary graph grammars, transferring the flow control from an auxiliary structure to the state. Hence, the main contribution of this paper is to permit the use of Rodin theorem prover to carry out the verification of properties for CGG specifications. This is possible through a mechanism where a controlled graph grammar is translated into a regular graph grammar using dependencies and conflicts between rules.

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Keywords

controlled graph grammar, dependent and conflicting rules, Graph grammar, Theoretical Computer Science, Computer Science(all)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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