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Calculating differences between models is an important and challenging task in Model Driven Engineering. Model differencing involves a number of steps starting with identifying matching model elements, calculating and representing their differences, and finally visualizing them in an appropriate way. In this paper, we provide an overview of the fundamental steps involved in the model differencing process and summarize the advantages and shortcomings of existing approaches for identifying matching model elements. To assist potential users in selecting one of the existing methods for the problem at stake, we investigate the trade-offs these methods impose in terms of accuracy and effort required to implement each one of them.

Abstract. In the context of Model Engineering, work has focused on operations such as model validation and model transformation. By contrast, other model management operations of significant importance remain underdeveloped. One of the least elaborated operations is model merging. In this paper we discuss the special requirements of model merging and introduce the Epsilon Merging Language (EML), a rule-based language, with tool support, for merging models of diverse metamodels and technologies. Moreover, we identify special cases of model merging that are of particular interest and provide a working example through which we demonstrate the practicality and usefulness of the proposed language.

Abstract. In their recent book, Mens and Demeyer state that ModelDriven Engineering introduces additional challenges for controlling and managing software evolution. Today, tools exist for generating model editors and for managing models with transformation, validation, merging and weaving. There is limited support, however, for model migration -a development activity in which instance models are updated in response to metamodel evolution. In this paper, we describe Epsilon Flock, a modelto-model transformation language tailored for model migration that contributes a novel algorithm for relating source and target model elements.To demonstrate its conciseness, we compare Flock to other approaches.

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International audienceAs Model-Driven Engineering (MDE) is increasingly applied to larger and more complex systems, the current generation of modelling and model management technologies are being pushed to their limits in terms of capacity and eciency. Additional research and development is imperative in order to enable MDE to remain relevant with industrial practice and to continue delivering its widely recognised productivity , quality, and maintainability benefits. Achieving scalabil-ity in modelling and MDE involves being able to construct large models and domain-specific languages in a systematic manner, enabling teams of modellers to construct and refine large models in a collaborative manner, advancing the state of the art in model querying and transformations tools so that they can cope with large models (of the scale of millions of model elements), and providing an infrastructure for ecient storage, indexing and retrieval of large models. This paper attempts to provide a research roadmap for these aspects of scalability in MDE and outline directions for work in this emerging research area