MISE 19 - Montreal, Canada/Posters
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A model family is a set of related models, in a given language, with commonalities and variabilities between individual models. Model families result from the evolution of models over time (i.e., versions) and/or variation over the space dimension, such as in (software) product lines. In situations where there is a large number of models in a family, it may become more efficient to analyze and reason about models, all at once, instead of analyzing each model separately. To achieve this, we propose the concept of union model as a first-class generic artifact that captures a model family (in both dimensions of variability) in a compact and exact manner, to enable efficient analysis. This paper provides a characterization of “model families” and “union models”, together with a simple graph-based formalization. It also presents how variability among members of a model family can be distinguished in union models using a spatio-temporal annotation language. We define several experiments to analyze the performance gains of using union models and conducted one of these experiments. Our preliminary empirical results show that the use of union models (rep-resenting multiple models) has the potential to increase analysis efficiency compared to the analysis of individual models. | A model family is a set of related models, in a given language, with commonalities and variabilities between individual models. Model families result from the evolution of models over time (i.e., versions) and/or variation over the space dimension, such as in (software) product lines. In situations where there is a large number of models in a family, it may become more efficient to analyze and reason about models, all at once, instead of analyzing each model separately. To achieve this, we propose the concept of union model as a first-class generic artifact that captures a model family (in both dimensions of variability) in a compact and exact manner, to enable efficient analysis. This paper provides a characterization of “model families” and “union models”, together with a simple graph-based formalization. It also presents how variability among members of a model family can be distinguished in union models using a spatio-temporal annotation language. We define several experiments to analyze the performance gains of using union models and conducted one of these experiments. Our preliminary empirical results show that the use of union models (rep-resenting multiple models) has the potential to increase analysis efficiency compared to the analysis of individual models. | ||
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'''Model Driven Generation of an Overall Scheduling in Cooperative Component Based Development''' <br> | '''Model Driven Generation of an Overall Scheduling in Cooperative Component Based Development''' <br> | ||
Revision as of 17:35, 13 March 2019
Posters
On the Characterization of Model Families and Union Models
Sanaa Alwidian, Daniel Amyot
A model family is a set of related models, in a given language, with commonalities and variabilities between individual models. Model families result from the evolution of models over time (i.e., versions) and/or variation over the space dimension, such as in (software) product lines. In situations where there is a large number of models in a family, it may become more efficient to analyze and reason about models, all at once, instead of analyzing each model separately. To achieve this, we propose the concept of union model as a first-class generic artifact that captures a model family (in both dimensions of variability) in a compact and exact manner, to enable efficient analysis. This paper provides a characterization of “model families” and “union models”, together with a simple graph-based formalization. It also presents how variability among members of a model family can be distinguished in union models using a spatio-temporal annotation language. We define several experiments to analyze the performance gains of using union models and conducted one of these experiments. Our preliminary empirical results show that the use of union models (rep-resenting multiple models) has the potential to increase analysis efficiency compared to the analysis of individual models.
Model Driven Generation of an Overall Scheduling in Cooperative Component Based Development
Arthur Strasser, Christoph Knieke, Andreas Rausch
A model family is a set of related models, in a given language, with commonalities and variabilities between individual models. Model families result from the evolution of models over time (i.e., versions) and/or variation over the space dimension, such as in (software) product lines. In situations where there is a large number of models in a family, it may become more efficient to analyze and reason about models, all at once, instead of analyzing each model separately. To achieve this, we propose the concept of union model as a first-class generic artifact that captures a model family (in both dimensions of variability) in a compact and exact manner, to enable efficient analysis. This paper provides a characterization of “model families” and “union models”, together with a simple graph-based formalization. It also presents how variability among members of a model family can be distinguished in union models using a spatio-temporal annotation language. We define several experiments to analyze the performance gains of using union models and conducted one of these experiments. Our preliminary empirical results show that the use of union models (rep-resenting multiple models) has the potential to increase analysis efficiency compared to the analysis of individual models.