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.
Margaret Hamilton is not only a software engineering pioneer but a model-driven one as well. Her work on the Universal Systems Language already embedded many of the key modeling concepts we use nowadays (platform-independence, code-generation,…).
A selection of visual modeling environments for data science, machine learning and smart apps. The fastest way to start building your own AI components and ML models
List of smart modeling tools, i.e. tools that include an AI assistant to help modelers write better models faster.
A discussion of architectural patterns from a modeling perspective. How modeling can help the organization of your information system? How (bad) modeling practices can damage it?
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.