Implicit Incremental Model Analyses and Transformations

When models of a system change, analyses based on them have to be reevaluated in order for the results to stay meaningful. In many cases, the time to get updated analysis results is critical. This thesis proposes multiple, combinable approaches and a new formalism based on category theory for implicitly incremental model analyses and transformations. The advantages of the implementation are validated using seven case studies, partially drawn from the Transformation Tool Contest (TTC)

Verifying SimpleGT Transformations Using an Intermediate Verification Language

Previously, we have developed the VerMTLr framework that allows rapid verifier construction for relational model transformation languages. VerMTLr draws on the Boogie intermediate verification language to systematically design a modular and reusable verifier. It also includes a modular formalisation of EMFTVM bytecode to ensure verifier soundness. In this work, we will illustrate how to adapt VerMTLr to design a verifier for the SimpleGT graph transformation language, which allows us to soundly prove the correctness of graph transformations. An experiment with the Pacman game demonstrates the feasibility of our approach

Verifying SimpleGT Transformations Using an Intermediate Verification Language

Previously, we have developed the VerMTLr framework that allows rapid verifier construction for relational model transformation languages. VerMTLr draws on the Boogie intermediate verification language to systematically design a modular and reusable verifier. It also includes a modular formalisation of EMFTVM bytecode to ensure verifier soundness. In this work, we will illustrate how to adapt VerMTLr to design a verifier for the SimpleGT graph transformation language, which allows us to soundly prove the correctness of graph transformations. An experiment with the Pacman game demonstrates the feasibility of our approach