Model Transformation Modularization as a Many-Objective Optimization Problem

Abstract

Abstract: Modular design is a desirable property for model transformations as it can significantly improve their comprehensibility, maintainability, reusability, and thus, their overall quality. Although language support for modularization of model transformations is emerging, model transformations are mostly created as monolithic artifacts containing a huge number of rules.

To tackle this problem, we propose an automated search-based approach to modularize model transformations based on higher-order transformations. Their application and execution is guided by our search framework which combines an in-place transformation engine and a search-based algorithm framework. We demonstrate the feasibility of our approach by using ATL as concrete transformation language and NSGA-III as search algorithm including well-known design metrics for the fitness functions to evaluate the generated clustering solutions. To validate our approach, we apply it to a comprehensive dataset of model transformations. As the study shows, ATL transformations can be modularized automatically, efficiently, and effectively by our approach.