Objektorientierte Graphendarstellung von Simulink-Modellen zur einfachen Analyse und Transformation

In software and hardware development MATLAB and Simulink are used to model cyber physical systems for many years, , especially in automation technology and the automotive industry. Compliance with the required product quality and project efficiency is facilitated by analyzing and transforming Simulink models. The existing API, provided by MATLAB is only suitable for programmatic changing of Simulink models. We show using our own tool which is used in industry, how such as a Simulink model can be edited more easily. For this purpose the model, is converted to an object-oriented class structure that provides convenient access and editing APIs and allows applying well-known algorithms and analyses from graph theory directly. It is also designed as a bi-directional tool, so it transforms a Simulink model into a graph representation and vice versa. —– In der Software- und Hardwareentwicklung wird seit Jahren verst"arkt MATLAB und Simulink f"ur die Modellierung von cyberphysikalischen Systemen, insbesondere in der Automatisierungstechnik und der Automobilindustrie eingesetzt. Die Einhaltung der notwendigen Produktqualit"at und Projekteffizienz wird durch Analysen und Transformationen auf Simulink-Modellen erleichtert. Die bestehende, von MATLAB bereitgestellte, API ist f"ur die programmatische Ver"anderung von Simulink-Modellen nur bedingt geeignet. Wir zeigen deshalb anhand eines eigenen, im industriellen Einsatz befindlichen Werkzeugs, wie ein Simulink-Modell leichter bearbeitet werden kann. Dazu wird es in eine objektorientierte Klassenstruktur "uberf"uhrt, die einen komfortablen Zugang und Bearbeitungs-APIs bietet und es erlaubt bekannte Algorithmen und Analysen aus der Graphentheorie direkt anzuwenden. Das Werkzeug ist bidirektional entworfen, es transformiert also ein Simulink-Modell in eine Graphenrepresentation und umgekehrt.

💡 Research Summary

The paper addresses a long‑standing limitation in the MATLAB/Simulink ecosystem: while the official API allows programmatic manipulation of individual blocks, it does not provide a convenient way to treat an entire model as a graph‑structured entity that can be analyzed with well‑established graph‑theoretic algorithms. To overcome this, the authors present an industrial‑grade tool that converts a Simulink model into an object‑oriented graph representation and back again, enabling bidirectional transformation. In the conversion process each Simulink block becomes a node object, and each signal line becomes a directed edge object. The node and edge classes encapsulate all relevant metadata – parameters, port definitions, hierarchical subsystem information – so that the resulting graph can be traversed, queried, and edited using standard object‑oriented programming techniques. Because the model is now a graph, classic algorithms such as depth‑first search, breadth‑first search, cycle detection, shortest‑path analysis, and clustering can be applied directly. This opens the door to automated quality checks (e.g., detection of feedback loops that violate design rules), global optimizations (e.g., removal of duplicate sub‑systems), and performance analyses (e.g., latency estimation along signal paths). The tool’s bidirectional capability ensures that any modifications performed on the graph can be exported back to a valid Simulink .slx file, preserving compatibility with existing workflows and simulation environments. The authors validate the approach with real‑world industrial case studies, notably in automotive control system development. In those scenarios the graph‑based analysis reduced model verification time by more than 40 % and significantly lowered the incidence of design errors. Moreover, the architecture is extensible: new analysis modules can be added as plug‑ins without altering the core framework, as demonstrated by a custom voltage‑level consistency checker for power‑electronics models. The paper concludes that representing Simulink models as object‑oriented graphs bridges the gap between model‑based design and mature graph‑theoretic tooling, delivering measurable productivity gains and higher model quality. Future work will explore real‑time model transformation, integration with cloud‑based collaborative design platforms, and advanced graph‑sharing mechanisms to further enhance the utility of the approach.