Model-Driven Engineering in the Large: Refactoring Techniques for Models and Model Transformation Systems
Model-Driven Engineering (MDE) is a software engineering paradigm that aims to increase the productivity of developers by raising the abstraction level of software development. It envisions the use of models as key artifacts during design, implementation and deployment. From the recent arrival o...
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|Zusammenfassung:||Model-Driven Engineering (MDE) is a software engineering paradigm that aims to increase the productivity of developers by raising the abstraction level of software development. It envisions the use of models as key artifacts during design, implementation and deployment. From the recent arrival of MDE in large-scale industrial software development – a trend we refer to as MDE in the large –, a set of challenges emerges: First, models are now developed at distributed locations, by teams of teams. In such highly collaborative settings, the presence of large monolithic models gives rise to certain issues, such as their proneness to editing conflicts. Second, in large-scale system development, models are created using various domain-specific modeling languages. Combining these models in a disciplined manner calls for adequate modularization mechanisms. Third, the development of models is handled systematically by expressing the involved operations using model transformation rules. Such rules are often created by cloning, a practice related to performance and maintainability issues. In this thesis, we contribute three refactoring techniques, each aiming to tackle one of these challenges. First, we propose a technique to split a large monolithic model into a set of sub-models. The aim of this technique is to enable a separation of concerns within models, promoting a concern-based collaboration style: Collaborators operate on the submodels relevant for their task at hand. Second, we suggest a technique to encapsulate model components by introducing modular interfaces in a set of related models. The goal of this technique is to establish modularity in these models. Third, we introduce a refactoring to merge a set of model transformation rules exhibiting a high degree of similarity. The aim of this technique is to improve maintainability and performance by eliminating the drawbacks associated with cloning. The refactoring creates variability-based rules, a novel type of rule allowing to capture variability by using annotations. The refactoring techniques contributed in this work help to reduce the manual effort during the refactoring of models and transformation rules to a large extent. As indicated in a series of realistic case studies, the output produced by the techniques is comparable or, in the case of transformation rules, partly even preferable to the result of manual refactoring, yielding a promising outlook on the applicability in real-world settings.|