by Johannes Dyck, Holger Giese
Abstract:
Graph transformation systems are a powerful formal model to capture model transformations or systems with infinite state space, among others. However, this expressive power comes at the cost of rather limited automated analysis capabilities. The general case of unbounded many initial graphs or infinite state spaces is only supported by approaches with rather limited scalability or expressiveness. In this paper we improve an existing approach for the automated verification of inductive invariants for graph transformation systems. By employing partial negative application conditions to represent and check many alternative conditions in a more compact manner, we can check examples with rules and constraints of substantially higher complexity. We also substantially extend the expressive power by supporting more complex negative application conditions and provide higher accuracy by employing advanced implication checks. The improvements are evaluated and compared with another applicable tool by considering three case studies.
Reference:
Inductive Invariant Checking with Partial Negative Application Conditions (Johannes Dyck, Holger Giese), In Graph Transformation (Francesco Parisi-Presicce, Bernhard Westfechtel, eds.), Springer, volume 9151, 2015.
Bibtex Entry:
@InProceedings{Dyck_Giese:2015,
AUTHOR = {Dyck, Johannes and Giese, Holger},
TITLE = {{Inductive Invariant Checking with Partial Negative Application Conditions}},
YEAR = {2015},
BOOKTITLE = {Graph Transformation},
VOLUME = {9151},
PAGES = {237-253},
EDITOR = {Parisi-Presicce, Francesco and Westfechtel, Bernhard},
SERIES = {LNCS},
ADDRESS = {Cham},
PUBLISHER = {Springer},
URL = {http://dx.doi.org/10.1007/978-3-319-21145-9_15},
PDF = {uploads/pdf/Dyck_Giese:2015_submission.pdf},
OPTacc_pdf = {},
ABSTRACT = {Graph transformation systems are a powerful formal model to capture model transformations or systems with infinite state space, among others. However, this expressive power comes at the cost of rather limited automated analysis capabilities. The general case of unbounded many initial graphs or infinite state spaces is only supported by approaches with rather limited scalability or expressiveness.
In this paper we improve an existing approach for the automated verification of inductive invariants for graph transformation systems. By employing partial negative application conditions to represent and check many alternative conditions in a more compact manner, we can check examples with rules and constraints of substantially higher complexity. We also substantially extend the expressive power by supporting more complex negative application conditions and provide higher accuracy by employing advanced implication checks.
The improvements are evaluated and compared with another applicable tool by considering three case studies.
}
}