Experimental assessment of combining pattern matching strategies with VIATRA2

As recent tool contests demonstrated graph transformation tools scale up to handle very large models for model transformations, thanks to recent advances in graph pattern matching techniques. In this paper, we assess the performance and capabilities of the Viatra2 model transformation framework by implementing the AntWorld case study of the GraBats 2008 graph transformation tool contest. First, we extend initial measurements carried out in Bergmann et al. (Proceedings of ICMT ’09, 2nd International Conference on Model Transformation, Springer, Berlin, 2009) to assess the effects of combining local search-based and incremental pattern matching strategies. Moreover, we also assess the performance characteristics of various language features of Viatra2 as well as the cost of certain model manipulation operations. We observe by experimentation how Viatra2 can scale up to large iteratively growing model sizes and focus on execution time and memory consumption. We believe that the results obtained from the benchmark example can set the course for further performance enhancement of Viatra2 and other future model transformation frameworks.