Metamodel Matching Research Articles

Model Transformation Development Using Automated Requirements Analysis, Metamodel Matching, and Transformation by Example

In this article, we address how the production of model transformations (MT) can be accelerated by automation of transformation synthesis from requirements, examples, and metamodels. We introduce a synthesis process based on metamodel matching, correspondence patterns between metamodels, and completeness and consistency analysis of matches. We describe how the limitations of metamodel matching can be addressed by combining matching with automated requirements analysis and model transformation by example (MTBE) techniques. We show that in practical examples a large percentage of required transformation functionality can usually be constructed automatically, thus potentially reducing development effort. We also evaluate the efficiency of synthesised transformations. Our novel contributions are: The concept of correspondence patterns between metamodels of a transformation. Requirements analysis of transformations using natural language processing (NLP) and machine learning (ML). Symbolic MTBE using “predictive specification” to infer transformations from examples. Transformation generation in multiple MT languages and in Java, from an abstract intermediate language.

Search-based metamodel matching with structural and syntactic measures

The use of different domain-specific modeling languages and diverse versions of the same modeling language often entails the need to translate models between the different languages and language versions. The first step in establishing a transformation between two languages is to find their corresponding concepts, i.e., finding correspondences between their metamodel elements. Although, metamodels use heterogeneous terminologies and structures, they often still describe similar language concepts. In this paper, we propose to combine structural metrics (e.g., number of properties per concept) and syntactic metrics to generate correspondences between metamodels. Because metamodel matching requires to cope with a huge search space of possible element combinations, we adapted a local and a global metaheuristic search algorithm to find the best set of correspondences between metamodels. The efficiency and effectiveness of our proposal is evaluated on different matching scenarios based on existing benchmarks. In addition, we compared our technique to state-of-the-art ontology matching and model matching approaches.

Towards A Semi-Automatic Transformation Process in MDA

Model Driven Engineering (MDE) has been proposed for supporting the development, maintenance and evolution of software systems. Model Driven Architecture (MDA), Software Factories, and the Eclipse Modeling Framework (EMF) are representative MDE approaches. These MDE approaches have concepts and techniques in common such as modeling, metamodels, and model transformation. Today, it is well recognized that model transformation is the most important of MDE approaches and is one of the most important operations in MDE. Despite the multitude of model transformation language proposals emerging from university and industry, these transformations are often created manually, which is a tedious and error-prone task and therefore an expensive process. In this paper, the authors propose an extended architecture that aims to semi-automate the process of transformation in the context of MDA. This architecture involves concepts and techniques of metamodel matching, inspired and borrowed from schema matching techniques that are an important issue in the database and ontology domain. This architecture is enforced by a methodology which details the different steps leading to a semi-automatic transformation process. Matching foundations and the authors’ approach for metamodel matching are discussed.