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In model-driven engineering, analogously to any software development practice, metamodel design must be accurate and performed by considering relevant quality factors, including maintainability, reusability, and understandability. The quality of metamodels might be compromised by the introduction of smells that can be the result of inappropriate design decisions. Detecting and resolving metamodel smells are a complex task. The existing approaches deal with this problem by supporting the identification and resolution of smells without providing the means to explicitly trace them with the quality attributes that can be potentially affected. In this paper, we present an approach to defining extensible catalogues of metamodel smells. Each smell can be linked to the corresponding quality attributes. Such links are exploited to automatically select only those smells that have to be necessarily resolved for enhancing the quality factors that are of interest for the modeler. The implementation of the approach is based on the Edelta language, and it has been validated on a corpus of metamodels retrieved from a publicly available repository.

Quality-Driven Detection and Resolution of Metamodel Smells