Measuring model complexity – Part 3: Introducing Complexity Analyzer

In our previous blog post, we learned how to measure model complexity. In this post, we explore how FlowComplexity can help us put theory into practice.

Introducing FlowComplexity

FlowComplexity is a standalone tool designed to evaluate the complexity of BPMN and CMMN models created in Flowable. It can be downloaded from the Tools section on the Flowable trial account page. Simply sign up for a free trial — no strings attached.

After signing in, you'll gain access to a variety of resources to begin your learning journey with Flowable. In the Tools section, alongside other utilities like Flowable Leap, you'll find FlowComplexity available for download. Just download the tool, extract the contents, and follow the instructions in the included README file to start analyzing your workflows.

Figure 1: FlowComplexity Main Page

The tool requires access to your Flowable Design instance to fetch and analyze models. To begin, click on "Connect Design" and enter the necessary credentials, as illustrated here:

Figure 2: Connect to Flowable Design

Once the analysis is complete, the tool displays a comprehensive list of models retrieved from Flowable Design, along with their respective complexity scores and a breakdown of the metrics that contribute to their complexity.

Figure 3: Model complexity breakdown

You can easily download the analyzed models or their associated metrics for further review.

Figure 4: Model XML

Additionally, the tool includes a dedicated "Metrics" section, providing detailed explanations and examples of all the complexity metrics for better understanding.

Figure 5: Metrics overview

What's next?

To conclude this series, we’ve explored the journey from understanding workflow complexity to building a comprehensive tool that evaluates and classifies models based on measurable metrics. These efforts aim to provide actionable insights, transforming raw data into meaningful guidance for workflow optimization. However, the true potential of complexity analysis lies not just in evaluation but in how these insights can be practically applied to enhance workflow design and execution. So what's next?

Making it open

The complexity analyzer serves as a valuable tool for modelers seeking to assess and improve both the readability and maintainability of designed models. Making this tool openly accessible empowers the modeling community to leverage data-driven insights for enhanced model design.

Providing in-design suggestion

Augmenting the modeling environment with real-time suggestions based on complexity analysis can aid modelers in creating more efficient and maintainable workflows. Imagine having the “AI Assistance” in design , which when you add gateway conditions for taking decision will smartly suggest to use decision task, or replacing your service tasks with new and improved service registry tasks.

Integrating with runtime information

By integrating complexity analysis with runtime information, organizations can gain holistic insights into model performance and resource utilization. And this integration facilitates proactive model optimization that promotes more efficient workflow execution at design stages. As we look to the future, the Complexity Analyzer has the potential to evolve in several exciting directions to help solidify its role as a crucial component in modern workflow management.

Let us know your thoughts about the tool. We are happy to hear your feedback.