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Models are primary artifacts in model-based, and especially, in model-driven software development processes. Therefore, software quality and quality assurance frequently leads back to the quality and quality assurance of the involved models. In our approach, we propose a model quality assurance process that is based on static model analysis and uses techniques like model metrics and model smells. Based on the outcome of the model analysis, appropriate model refactoring steps are performed. Appropriate tools support the included techniques, i.e. metrics, smells, and refactorings, for models that are based on the Eclipse Modeling Framework (EMF). In this paper, we present the integration of the two model quality tools EMF Smell and EMF Refactor. This integration provides modelers with a quick and easy way to erase model smells by automatically suggesting appropriate model refactorings, and to get warnings in cases where new model smells come in by applying a certain refactoring.

Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation . The key idea is to encode a set of similar rules into a compact representation, called variability-based rule . We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact.

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