Abstract
Many scenarios require flexible model transformations as their execution should of course produce models with the best possible quality. At the same time, transformation problems often span a very large search space with respect to possible transformation results. Thus, guidance for transformation executions to find good solutions without enumerating the complete search space is a must.
This paper presents MOMoT, a tool combining the power of model transformation engines and meta-heuristics search algorithms. This allows to develop model transformation rules as known from existing approaches, but for guiding their execution, the transformation engineers only have to specify transformation goals, and then the search algorithms take care of orchestrating the set of transformation rules to find models best fulfilling the stated, potentially conflicting transformation goals. For this, MOMoT allows to use a variety of different search algorithms. MOMoT is available as an open-source Eclipse plug-in providing a non-intrusive integration of the Henshin graph transformation framework and the MOEA search algorithm framework.
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Notes
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Zero is assigned to the result of a division whenever its denominator is zero.
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Please note that MOMoT supports different Henshin transformation units and more complex transformations. However, for the purpose of the tool demonstration, we simply use one transformation rule and put the emphasis on the MOMoT specific features.
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Fleck, M., Troya, J., Wimmer, M. (2016). Search-Based Model Transformations with MOMoT. In: Van Gorp, P., Engels, G. (eds) Theory and Practice of Model Transformations. ICMT 2016. Lecture Notes in Computer Science(), vol 9765. Springer, Cham. https://doi.org/10.1007/978-3-319-42064-6_6
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