Towards Automated Semantic Grouping in Workflows for Multi-Disciplinary Analysis
This addresses maintainability issues for researchers and engineers using visual development environments like RCE, but it is incremental as it builds on existing graph clustering methods.
The paper tackled the problem of deteriorating maintainability in multidisciplinary tool workflows by automatically identifying groups of closely related tools using graph clustering algorithms, with results strongly indicating the approach can yield such groups but requires customization for specific workflow styles.
When designing multidisciplinary tool workflows in visual development environments, researchers and engineers often combine simulation tools which serve a functional purpose and helper tools that merely ensure technical compatibility by, e.g., converting between file formats. If the development environment does not offer native support for such groups of tools, maintainability of the developed workflow quickly deteriorates with an increase in complexity. We present an approach towards automatically identifying such groups of closely related tools in multidisciplinary workflows implemented in RCE by transforming the workflow into a graph and applying graph clustering algorithms to it. Further, we implement this approach and evaluate multiple clustering algorithms. Our results strongly indicate that this approach can yield groups of closely related tools in RCE workflows, but also that solutions to this problem will have to be tailor-made to each specific style of workflow design.