Managing design-time uncertainty in OCL expressions
We propose an extension to the OCL notation to be able to model constraints even when we do not have enough domain information to be completely precise about the rule we are modeling
Managing Belief Uncertainty in Models
We show how domain experts can individually reason about their models and combine their opinions to reach a consensus on the models and objects they are modeling
Belief Uncertainty in Software Models
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.
Expressing Confidence in Models and Model Transformation
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.
Explicating and reasoning with model uncertainty
Our specification of models with uncertainty implicitly encodes a set of alternative possible models where we are not sure which is the correct one
Modeling: all you need to know
