Graph transformation benchmarks

Graph transformation benchmarks Benchmarking has a key role in decision making processes when a choice has to be made between several alternatives. In order to fill this role, system designers should get a proper view on the system, which means that characteristics of the system have to be measured under different circumstances (i.e. by using several parameter combinations for measurements). Graph transformation provides a pattern and rule based manipulation of graph models. Since there is a couple of fields where graph based models can be used, graph transformation can be considered as a widely applicable approach. However, despite the large variety of graph transformation tools (AGG, Fujaba, GReAT, Groove, PROGRES, Viatra, up to this point there did not exist any collection of benchmarks for comparing such tools. The aim of this webpage is to bridge this gap and to provide a description of a basic set of benchmark examples together with scenarios for which the benchmarks can be used. Moreover, our initiative includes a quantitative comparison of the performance of graph transformation tools by defining certain parameter settings and optimization possibilities for different test cases that are requested to be implemented by tool providers. In case of graph transformation benchmarks the sole measurable feature, which composes the base of comparison in turn, is the execution time of pattern matching and updating phases. (Note that the time needed for generating the initial models does not take part in measurements, and thus, this topic is not discussed here.) Execution times are measured for several tools and on different test sets, while the underlying hardware remains the same for all benchmarks.

References in zbMATH (referenced in 11 articles , 1 standard article )

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  1. Bergmann, Gábor; Ráth, István; Szabó, Tamás; Torrini, Paolo; Varró, Dániel: Incremental pattern matching for the efficient computation of transitive closure (2012)
  2. Biermann, Enrico; Ermel, Claudia; Lambers, Leen; Prange, Ulrike; Runge, Olga; Taentzer, Gabriele: Introduction to AGG and EMF Tiger by modeling a Conference Scheduling System (2010)
  3. Horváth, Ákos; Bergmann, Gábor; Ráth, István; Varró, Dániel: Experimental assessment of combining pattern matching strategies with VIATRA2 (2010)
  4. Rensink, Arend: The edge of graph transformation -- graphs for behavioural specification (2010)
  5. Jackson, Ethan; Sztipanovits, Janos: Formalizing the structural semantics of domain-specific modeling languages (2009)
  6. Bergmann, Gábor; Horváth, Ákos; Ráth, István; Varró, Dániel: A benchmark evaluation of incremental pattern matching in graph transformation (2008)
  7. Varró, Gergely: Implementing an EJB3-specific graph transformation plugin by using database independent queries (2008)
  8. Becker, Simon M.; Herold, Sebastian; Lohmann, Sebastian; Westfechtel, Bernhard: A graph-based algorithm for consistency maintenance in incremental and interactive integration tools (2007)
  9. Agrawal, Aditya; Karsai, Gabor; Neema, Sandeep; Shi, Feng; Vizhanyo, Attila: The design of a language for model transformations (2006)
  10. Geiß, Rubino; Batz, Gernot Veit; Grund, Daniel; Hack, Sebastian; Szalkowski, Adam: GrGen: A fast SPO-based graph rewriting tool (2006)
  11. Mens, Tom; Van Gorp, Pieter; Varró, Dániel; Karsai, Gabor: Applying a model transformation taxonomy to graph transformation technology. (2006)