Model-based Block Diagram Recognition for Model Visualization Verification

In safety-critical applications, domain-specific modeling (DSM) remains insufficiently automated, requiring significant effort. One critical issue is ensuring correct visualization without relying on manual verification processes. In other words: "is what you see really what you get?" This paper proposes a novel approach for the automated verification of visualizations, specifically targeting block diagrams, which are a common representation in DSM. Our approach utilizes image processing techniques to recognize block diagrams and compare them with the original models to detect potential deviations. We demonstrate the effectiveness of our proof of concept through a use case involving a single graphical domain-specific language. The implementation successfully detects all 14 visualization error types in the tested diagrams, highlighting its potential to improve reliability and reducing manual verification efforts in safety-critical DSM environments.