We propose to assign a degree of belief to model statements, which is expressed by a probability (called credence, in statistical terms) that represents a quantification of such a subjective degree of belief. We discuss how it can be represented using current modeling notations, and how to operate with it in order to make informed decisions.
A new approach to partially generate graphical user interfaces from business process models based on a set of common BPMN patterns.
ARGON helps you to define your cloud infrastructure provisioning (via its own DSL) and, based on that, automate the migration process from one cloud provider to the other
Validate efficiently large models using OCL rewriting techniques and optimized database query generation using the Mogwaï tool.
Models need to represent the reality as accurately as possible. Nevertheless, complex systems are subject to uncertainty something difficult to express with plain UML. We propose a way to represent uncertainty on software models. Our uncertainty values can then be propagated through model transformations to evaluate the impact on other parts of the system.
We present a novel robust hashing mechanism for models. Robust hashing algorithms (i.e. hashing algorithms that generate similar outputs from similar input data) are useful as a key building block in intellectual property protection, authenticity assessment and fast comparison and retrieval solutions
