Building measure-based prediction models for UML class diagram maintainability
Empirical Software Engineering
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This paper presents an architecture to perform model-driven transformations that are guided by quality attributes. The main goal of the architecture is to define a set of artifacts and a process for specifying and executing model transformations in which the selection of alternative transformations is done based on quality attributes. Specifically, we focus on how to associate quality attributes to the different alternative transformations and how this information can be taken into account in an automated transformation process to obtain software artifacts with the desired quality attributes. A case study illustrates the selection of alternative transformations for relationships between classes and how these transformations affect the appropriateness recognizability and changeability of the target UML class models.