Toward a declarative language to generate explorable sets of models
Model transformation has proven to be an effective technique to produce target models from source models. Most transformation approaches focus on generating a single target model from a given source model. However there exists situations where a collection of target models is preferred over a single one. Such situations arise when some choices cannot be encoded in the transformation.
 In this paper, we introduce an approach that combines model transformation and constraints programming to generate explorable sets of target models from source models. Our approach is built around the notion of the bridge variable that binds target model properties to decision variables. To help users apply the approach, we also introduce a declarative language to write such transformations. We evaluate our approach and language on a case study for diagram visualization.