摘要
Graph transformation systems have become a general formal modeling language to describe many models in software development process.Behavioral modeling of dynamic systems and model-to-model transformations are only a few examples in which graphs have been used to software development.But even the perfect graph transformation system must be equipped with automated analysis capabilities to let users understand whether such a formal specification fulfills their requirements.In this paper,we present a new solution to verify graph transformation systems using the Bogor model checker.The attributed graph grammars(AGG)-like graph transformation systems are translated to Bandera intermediate representation(BIR),the input language of Bogor,and Bogor verifies the model against some interesting properties defined by combining linear temporal logic(LTL) and special-purpose graph rules.Experimental results are encouraging,showing that in most cases our solution improves existing approaches in terms of both performance and expressiveness.
Graph transformation systems have become a general formal modeling language to describe many models in software development process. Behavioral modeling of dynamic systems and model-to-model transformations are only a few examples in which graphs have been used to software development. But even the perfect graph transformation system must be equipped with automated analysis capabilities to let users understand whether such a formal specification fulfills their requirements. In this paper, we present a new solution to verify graph transformation systems using the Bogor model checker. The attributed graph grammars （AGG）-like graph transformation systems are translated to Bandera intermediate representation （BIR）, the input language of Bogor, and Bogor verifies the model against some interesting properties defined by combining linear temporal logic （LTL） and special-purpose graph rules. Experimental results are encouraging, showing that in most cases our solution improves existing approaches in terms of both performance and expressiveness.
