International Conference

Generating Assertion Code from OCL: A Transformational Approach Based on Similarities of Implementation Languages

Rodion Moiseev, Shinpei Hayashi, Motoshi Saeki

In Proceedings of the ACM/IEEE 12th International Conference on Model Driven Engineering Languages and Systems (MODELS 2009), Lecture Notes in Computer Science, vol. 5795, pp. 650–664, Denver, Colorado, USA, October 2009.

Abstract

The Object Constraint Language (OCL) carries a platform independent characteristic allowing it to be decoupled from implementation details, and therefore it is widely applied in model transformations used by model-driven development techniques. However, OCL can be found tremendously useful in the implementation phase aiding assertion code generation and allowing system verification. Yet, taking full advantage of OCL without destroying its platform independence is a difficult task. This paper proposes an approach for generating assertion code from OCL constraints by using a model transformation technique to abstract language specific details away from OCL high-level concepts, showing wide applicability of model transformation techniques. We take advantage of structural similarities of implementation languages to describe a rewriting framework, which is used to easily and flexibly reformulate OCL constraints into any target language, making them executable on any platform. A tool is implemented to demonstrate the effectiveness of this approach.

BibTeX

@inproceedings{rodion-models2009,
    author = {Rodion Moiseev and Shinpei Hayashi and Motoshi Saeki},
    title = {Generating Assertion Code from OCL: A Transformational Approach Based on Similarities of Implementation Languages},
    booktitle = {Proceedings of the ACM/IEEE 12th International Conference on Model Driven Engineering Languages and Systems},
    pages = {650--664},
    doi = {10.1007/978-3-642-04425-0_52},
    year = 2009,
}
Type
International Conference
Conference
MODELS 2009
Location
Denver, Colorado, USA
Presented
October 9, 2009
Volume / Pages
vol. 5795, pp. 650–664
Acceptance rate
34+12/211 (16%)
DOI
10.1007/978-3-642-04425-0_52