Abstract
Numerous modeling languages have adapted a graphical syntax that emphasizes control flow or state rather than data flow. We here refer to these as state diagrams, which include classic control flow diagrams as well as for example Statecharts. State diagrams are usually considered to be fairly easy to comprehend and to facilitate the understanding of the general system behavior. However, finding data dependencies between concurrent activities can be difficult as these dependencies must be deduced by matching textual variable references.
We here investigate how to extract data flow information from state diagrams and how to make that information more accessible to the modeler. A key enabler is automatic layout, which allows to automatically create dynamic, customized views from a given model. To set the stage, we propose a taxonomy of state and data-flow based modeling and viewing approaches. We then compare traditional, static view approaches with dynamic views. We present implementation results based on the open-source Ptolemy and KIELER frameworks and the Eclipse Layout Kernel.
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Acknowledgements
We thank the participants of the 2008 and 2017 editions of the model railway project conducted at Kiel University for the SCADE and SCCharts models they contributed. We also thank the contributors to the open source projects such as Ptolemy, as well as KIELER, ELK, Xtext and numerous other Eclipse projects, that made this work possible.
This work has been supported in part by the German Science Foundation, as part of the Precision-Timed Synchronous Reactive Processing (PRETSY2, DFG HA 4407/6-2) and Compact Graph Drawing with Port Constraints (ComDraPor, DFG HA 4407/8-1) projects.
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Wechselberg, N., Schulz-Rosengarten, A., Smyth, S., von Hanxleden, R. (2018). Augmenting State Models with Data Flow. In: Lohstroh, M., Derler, P., Sirjani, M. (eds) Principles of Modeling. Lecture Notes in Computer Science(), vol 10760. Springer, Cham. https://doi.org/10.1007/978-3-319-95246-8_28
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