By Hartmut Ehrig, Claudia Ermel, Ulrike Golas, Frank Hermann
This e-book is a complete rationalization of graph and version transformation. It encompasses a targeted creation, together with easy effects and purposes of the algebraic idea of graph differences, and references to the historic context. Then more often than not half the booklet includes certain chapters on M-adhesive different types, M-adhesive transformation structures, and multi-amalgamated adjustments, and version transformation in line with triple graph grammars. within the ultimate a part of the ebook the authors research program of the innovations in a variety of domain names, together with chapters on case experiences and gear aid.
The publication may be of curiosity to researchers and practitioners within the components of theoretical computing device technology, software program engineering, concurrent and dispensed platforms, and visible modelling.
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Extra info for Graph and Model Transformation: General Framework and Applications
The second dimension separates horizontal model transformations that do not change the level of abstraction from vertical model transformations, which explicitly do change the level of abstraction. Examples of model transformations in these dimensions as listed in [MG06] are: model refactoring (endogeneous, horizontal), formal model refinement (exogeneous, horizontal), language migration (endogeneous, vertical) and code generation (exogeneous, vertical). In MDD, model transformations are (partially) automated.
Additionally, it can be required that MT reaches all models MT ∈ LT . 48 3 Model Transformation 4. Functional Behaviour: For each source model MS ∈ LS , the model transformation MT will always terminate and lead to the same resulting target model MT . In the second dimension, we treat nonfunctional aspects. They concern usability and applicability properties of model transformations. Depending on the application domain, some of the following challenges may be required in addition to the functional ones listed above.
MT ∈ LT . 2. Semantical Correctness: The semantics of each model MS ∈ LS that is transformed by MT has to be preserved or reflected, respectively. 3. Completeness: The model transformation MT can be performed on each model MS ∈ LS . Additionally, it can be required that MT reaches all models MT ∈ LT . 48 3 Model Transformation 4. Functional Behaviour: For each source model MS ∈ LS , the model transformation MT will always terminate and lead to the same resulting target model MT . In the second dimension, we treat nonfunctional aspects.