The quality of process descriptions in research is essential to reproducibility and theory building.
We suggest that model-based analysis can contribute to the improvement and concise communication of such descriptions. We demonstrate this by applying a model-based framework – PROVE – to restructure and analyze several process descriptions.
Specification of sequence diagrams is not easy. They are more complex than they appear to be and support provided by most UML modeling tools is often limited (e.g. no advanced checking).
Quality aspects of an API (availability, performance,…) are key aspects to take into account when deciding which API to choose. Our testing framework provides some insights on these non-functional properties as they are typically not disclosed
When testing or validating a model we need a diverse set of instances that helps us to analyze the different ways such model can be satisfied. Our work uses classifying terms and constraint strengthening to generate such diverse set.
We present our ecosystem of tools to facilitate the automatic discovery, merging, quality assurance and code-generation of REST APIs, relying on standard specifications like OpenAPI and OData.
We propose to employ graph kernels for clustering software modeling artifacts. Among other benefits, this would improve the efficiency and usability of a variety of software modeling activities, e.g., design space exploration, testing or verification and validation.
