Graph transformation has a wide range of applications areas like massively parallel computing, bioinformatics, bio-computing, DNA-computing, software architecture, model transformation, model based software development, development of meta CASE tools, graph based models of computations, knowledge-based systems, pattern generation, picture processing, layout algorithms, software quality, and language technology.

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Our transductive graph transformation approach has many advantouges: Transductive rules work with two tapes, one for reading and one for writing. These rules can be applied in parallel and need only two levels of representation. Since the input of transductive rules stay stable the rules create a static relation-ship between the symbols of the two levels. Transductive rules have access to both levels and their application is determined by specifying their context and not by ordering. This is also the reason that transductive rules can be bidirectional.