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Fine-Tuning Model Transformation: Change Propagation in Context of Consistency, Completeness, and Human Guidance.
by Alexander Egyed, Andreas Demuth, Achraf Ghabi, Roberto Erick Lopez-Herrejon, Patrick Mäder, Alexander Nöhrer, Alexander Reder
Abstract:
An important role of model transformation is in exchanging modeling information among diverse modeling languages. However, while a model is typically constrained by other models, additional information is often necessary to transform said models entirely. This dilemma poses unique challenges for the model transformation community. To counter this problem we require a smart transformation assistant. Such an assistant should be able to combine information from diverse models, react incrementally to enable transformation as information becomes available, and accept human guidance – from direct queries to understanding the designer(s) intentions. Such an assistant should embrace variability to explicitly express and constrain uncertainties during transformation – for example, by transforming alternatives (if no unique transformation result is computable) and constraining these alternatives during subsequent modeling. We would want this smart assistant to optimize how it seeks guidance, perhaps by asking the most beneficial questions first while avoiding asking questions at inappropriate times. Finally, we would want to ensure that such an assistant produces correct transformation results despite the presence of inconsistencies. Inconsistencies are often tolerated yet we have to understand that their presence may inadvertently trigger erroneous transformations, thus requiring backtracking and/or sandboxing of transformation results. This paper explores these and other issues concerning model transformation and sketches challenges and opportunities.
Reference:
Alexander Egyed, Andreas Demuth, Achraf Ghabi, Roberto Erick Lopez-Herrejon, Patrick Mäder, Alexander Nöhrer, Alexander Reder, "Fine-Tuning Model Transformation: Change Propagation in Context of Consistency, Completeness, and Human Guidance.", pp. 1-14, 2011.
Bibtex Entry:
@Conference{DBLP:conf/icmt/EgyedDGLMNR11,
Title = {Fine-Tuning Model Transformation: Change Propagation in Context of Consistency, Completeness, and Human Guidance.},
Author = {Alexander Egyed and Andreas Demuth and Achraf Ghabi and Roberto Erick Lopez-Herrejon and Patrick Mäder and Alexander Nöhrer and Alexander Reder},
Booktitle = {4th International Conference on Theory and Practice on Model Transformation (ICMT), Zürich, Switzerland},
Year = {2011},
Pages = {1-14},
Abstract = {An important role of model transformation is in exchanging modeling information among diverse modeling languages. However, while a model is typically constrained by other models, additional information is often necessary to transform said models entirely. This dilemma poses unique challenges for the model transformation community. To counter this problem we require a smart transformation assistant. Such an assistant should be able to combine information from diverse models, react incrementally to enable transformation as information becomes available, and accept human guidance – from direct queries to understanding the designer(s) intentions. Such an assistant should embrace variability to explicitly express and constrain uncertainties during transformation – for example, by transforming alternatives (if no unique transformation result is computable) and constraining these alternatives during subsequent modeling. We would want this smart assistant to optimize how it seeks guidance, perhaps by asking the most beneficial questions first while avoiding asking questions at inappropriate times. Finally, we would want to ensure that such an assistant produces correct transformation results despite the presence of inconsistencies. Inconsistencies are often tolerated yet we have to understand that their presence may inadvertently trigger erroneous transformations, thus requiring backtracking and/or sandboxing of transformation results. This paper explores these and other issues concerning model transformation and sketches challenges and opportunities.},
Doi = {10.1007/978-3-642-21732-6_1},
File = {Fine-Tuning Model Transformation:Conferences\\ICMT 2011 - Fine-Turing Model Transformation\\Fine-Tuning Model Transformation.pdf:PDF},
Keywords = {change, transformation, consistency, variability, FWF P23115-N23, FWF M1268-N23, EU IEF 254965},
Slides = {Egyed Keynote ICMT - Fine-Tuning Transformation.pdf}
}Powered by bibtexbrowser