Automated Generation of Consistent Models with Structural and Attribute Constraints
Title | Automated Generation of Consistent Models with Structural and Attribute Constraints |
Publication Type | Conference Paper |
Year of Publication | 2020 |
Authors | Semeráth, O., Babikian, A. A., Li, A., Marussy, K., and Varró, D. |
Editor | Syriani, E., and Sahraoui, H. |
Conference Name | 23rd International Conference on Model Driven Engineering Languages and Systems |
Publisher | ACM / IEEE |
Conference Location | Canada |
Keywords | model generation, partial modeling, SMT-solvers |
Abstract | Automatically synthesizing consistent models is a key prerequisite for many testing scenarios in autonomous driving or software tool validation where model-based systems engineering techniques are frequently used to ensure a designated coverage of critical cornercases. From a practical perspective, an inconsistent model is irrelevant as a test case (e.g. false positive), thus each synthetic model needs to simultaneously satisfy various structural and attribute well-formedness constraints. While different logic solvers or dedicated graph solvers have recently been developed, they fail to handle either structural or attribute constraints in a scalable way.
In the current paper, we combine a structural graph solver that uses partial models with an SMT-solver to automatically derive models which simultaneously fulfill structural and attribute constraints while key theoretical properties of model generation |
Notes | Artifacts (tool and measurement results) are availables as a virtual machine at https://zenodo.org/record/3950552 |
DOI | 10.1145/3365438.3410962 |