Multi-objective model transformation chain exploration with MOMoT

• Published in: Information and software technology Vol. 174; p. 107500
• Main Authors: Eisenberg, Martin, Sahay, Apurvanand, Di Ruscio, Davide, Iovino, Ludovico, Wimmer, Manuel, Pierantonio, Alfonso
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2024
• Subjects: Model repository; Model-driven optimization; Multi-objective graph traversal; Transformation chain; Model-driven optimization; Transformation chain; Multi-objective graph traversal; Model repository
• ISSN: 0950-5849; 1873-6025
• DOI: 10.1016/j.infsof.2024.107500

The increasing complexity of modern systems leads to an increasing amount of artifacts that are used along the model-based software and systems development lifecycle. This also includes model transformations, which serve for mapping models between representations, e.g., for verification and validation purposes. Model repositories manage this variety of artifacts and promote reusability, but should also enable the bundling of compatible artifacts. Therefore, model transformations should be reused and arranged into transformation chains to support more complex transformation scenarios. The resulting transformation should correspond to the user’s interest in terms of quality criteria such as model coverage, transformation coverage, and number of transformation steps, thus assembling such chains becomes a multi-objective problem. A novel multi-objective approach for exploring possible transformation chains residing in model repositories is presented. MOMoT, a model-driven optimization framework, is leveraged to explore the transformation space spanned by the repository. For demonstration, three differently populated repositories are considered. We have extended MOMoT with an exhaustive, multi-objective search that explores the entire model transformation space defined by graph transformation rules, allowing all possible transformation chains to be considered as solution. Accordingly, the optimal solutions were identified in the demonstration cases with negligible computation time. The approach assists modelers when there are multiple chains for transforming an input model to a specified output model to consider. Our evaluation shows that the approach elicits all legitimate transformation chains, thus enabling the modelers to consider trade-offs in view of multiple criteria selection.