Termination Criteria for Model Transformation

Model Transformation has become central to most software engineering activities. It refers to the process of modifying a (usually graphical) model for the purpose of analysis (by its transformation to some other domain), optimization, evolution, migration or even code generation. In this work, we sh...

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Bibliographic Details
Published inFundamental Approaches to Software Engineering pp. 49 - 63
Main Authors Ehrig, Hartmut, Ehrig, Karsten, de Lara, Juan, Taentzer, Gabriele, Varró, Dániel, Varró-Gyapay, Szilvia
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Computer science; control theory; systems
Exact sciences and technology
Graph Grammar
Graph Transformation
Graph Transformation Rule
Injective Morphism
Model Transformation
Software
Software engineering
Code generation
Model-based reasoning
Formal method
Software development
Termination problem
Expert system
Functional analysis
Knowledge base
Graph theory
Modeling
Optimization
State diagram
Graph transformation
Software engineering
Petri net
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Summary:Model Transformation has become central to most software engineering activities. It refers to the process of modifying a (usually graphical) model for the purpose of analysis (by its transformation to some other domain), optimization, evolution, migration or even code generation. In this work, we show termination criteria for model transformation based on graph transformation. This framework offers visual and formal techniques based on rules, in such a way that model transformations can be subject to analysis. Previous results on graph transformation are extended by proving the termination of a transformation if the rules applied meet certain criteria. We show the suitability of the approach by an example in which we translate a simplified version of Statecharts into Petri nets for functional correctness analysis.
Bibliography:Juan de Lara, Dániel Varró and Szilvia Varró-Gyapay were partially supported by the Segravis Research Training Network.
ISBN:354025420X
9783540254201
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-31984-9_5