ABSTRACT
Model Driven Engineering (MDE) is a methodology that aims to simplify the process of designing complex systems, by using models as an abstract representation of the underlying system.
This methodology allows domain experts to more easily focus on system design, where their knowledge is more useful, without having to work with the system implementation complexities. Search Based Model Engineering applies MDE concepts to optimisation problems. The goal is to simplify the process of solving optimisation problems for domain experts, by abstracting the complexity of solving optimisation problems and allowing them to focus on the domain level issues..
In this tool demostration we present MDEOptimiser (MDEO), a tool for specifying and solving optimisation problems using MDE. With MDEO the user can specify optimisation problems using a simple DSL. The tool can run evolutionary optimisation algorithms that use models as an encoding for population members and model transformations as search operators. We showcase the functionality of the tool using a number of case studies. We aim to show that with MDEO, specifying optimisation problems becomes a less complex task compared to custom implementations.
- Hani Abdeen, Dániel Varró, Houari Sahraoui, András Szabolcs Nagy, Csaba Debreceni, Ábel Hegedüs, and Ákos Horváth. {n. d.}. Multi-objective Optimization in Rule-based Design Space Exploration. 289--300. Google ScholarDigital Library
- Thorsten Arendt, Enrico Biermann, Stefan Jurack, Christian Krause, and Gabriele Taentzer. 2010. Henshin: advanced concepts and tools for in-place EMF model transformations. Model Driven Engineering Languages and Systems (2010), 121--135. Google ScholarDigital Library
- I. Boussaïd, P. Siarry, and M. Ahmed-Nacer. 2017. A survey on search-based model-driven engineering. Automated Software Engineering 24, 2 (2017), 233--294. Google ScholarDigital Library
- Frank R Burton and Simon Poulding. 2013. Complementing metaheuristic search with higher abstraction techniques. In Proceedings of the 1st International Workshop on Combining Modelling and Search-Based Software Engineering. IEEE Press, 45--48. Google ScholarDigital Library
- Alberto Rodrigues da Silva. 2015. Model-driven engineering: A survey supported by the unified conceptual model. Computer Languages, Systems & Structures 43 (2015), 139--155. Google ScholarDigital Library
- Dionysios Efstathiou, James R. Williams, and Steffen Zschaler. 2014. Crepe Complete: Multi-objective optimisation for your models. In Proc. 1st Int'l Workshop on Combining Modelling with Search- and Example-Based Approaches (CMSEBA'14).Google Scholar
- Martin Fleck, Javier Troya, and Manuel Wimmer. 2015. Marrying search-based optimization and model transformation technology. Proc. of NasBASE (2015).Google Scholar
- Martin Fleck, Javier Troya, and Manuel Wimmer. 2016. The Class Responsibility Assignment Case. In Proceedings of the 9th Transformation Tool Contest @STAF (CEUR Workshop Proceedings), Vol. 1758. 1--8. http://ceur-ws.org/Vol-1758/paper1.pdfGoogle Scholar
- Abel Hegedüs, Ákos Horváth, István Ráth, and Dániel Varró. {n. d.}. A Model-driven Framework for Guided Design Space Exploration. 173--182.Google Scholar
- Lorenzo Mandow, Jose Antonio Montenegro, and Steffen Zschaler. 2016. Mejora de una representación genética genérica para modelos. In Actas de la XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2016). in press.Google Scholar
- Tom Mens and Pieter Van Gorp. 2006. A taxonomy of model transformation. Electronic Notes in Theoretical Computer Science 152 (2006), 125--142. Google ScholarDigital Library
- Kenneth S Rubin. 2012. Essential Scrum. Addison-Wesley.Google Scholar
- Daniel Strüber. 2017. Generating Efficient Mutation Operators for Search-Based Model-Driven Engineering. Springer International Publishing, Cham, 121--137.Google Scholar
- James R. Williams. 2013. A novel representation for search-based model-driven engineering. Ph.D. Dissertation. University of York, UK. http://etheses.whiterose.ac.uk/5155/Google Scholar
Index Terms
- MDEoptimiser: a search based model engineering tool
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