Distributed Model-to-Model Transformation with ATL on MapReduce

Amine Benelallam (1, 2) , Abel Gómez (1, 2) , Massimo Tisi (1, 2) , Jordi Cabot (3)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 ATLANMOD - Modeling Technologies for Software Production, Operation, and Evolution
3 UNIROUEN - Université de Rouen Normandie

Efficient processing of very large models is a key requirement for the adoption of Model-Driven Engineering (MDE) in some industrial contexts. One of the central operations in MDE is rule-based model transformation (MT). It is used to specify manipulation operations over structured data coming in the form of model graphs. However, being based on com-putationally expensive operations like subgraph isomorphism, MT tools are facing issues on both memory occupancy and execution time while dealing with the increasing model size and complexity. One way to overcome these issues is to exploit the wide availability of distributed clusters in the Cloud for the distributed execution of MT. In this paper, we propose an approach to automatically distribute the execution of model transformations written in a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. We show how the execution semantics of ATL can be aligned with the MapReduce computation model. We describe the extensions to the ATL transformation engine to enable distribution, and we experimentally demonstrate the scalability of this solution in a reverse-engineering scenario.

Mots clés: Model Transformation; Distributed Computing; MapReduce; ATL; Language Engineering

Domaines: Informatique et langage [cs.CL]; Génie logiciel [cs.SE]

Amine Benelallam, Abel Gómez, Massimo Tisi, Jordi Cabot. Distributed Model-to-Model Transformation with ATL on MapReduce. Proceedings of 2015 ACM SIGPLAN International Conference on Software Language Engineering (SLE 2015), Oct 2015, Pittsburgh, United States. ⟨hal-01215228⟩