Exploring Complexity: An Extended Study of Formal Properties for Process Model Complexity Measures

TL;DR

This paper extends and compares various process model complexity measures, adapting them to workflow nets and analyzing their formal properties to guide analysts in selecting appropriate measures.

Contribution

It adapts existing complexity measures to workflow nets and evaluates their formal properties, providing insights for better measure selection.

Findings

Not all properties are mandatory for complexity measures.

Some properties influence the behavior of process discovery algorithms.

The study aids in choosing suitable complexity measures for different use-cases.

Abstract

A good process model is expected not only to reflect the behavior of the process, but also to be as easy to read and understand as possible. Because preferences vary across different applications, numerous measures provide ways to reflect the complexity of a model with a numeric score. However, this abundance of different complexity measures makes it difficult to select one for analysis. Furthermore, most complexity measures are defined for BPMN or EPC, but not for workflow nets. This paper is an extended analysis of complexity measures and their formal properties. It adapts existing complexity measures to the world of workflow nets. It then compares these measures with a set of properties originally defined for software complexity, as well as new extensions to it. We discuss the importance of the properties in theory by evaluating whether matured complexity measures should fulfill them or whether they are optional. We find that not all inspected properties are mandatory, but also demonstrate that the behavior of evolutionary process discovery algorithms is influenced by some of these properties. Our findings help analysts to choose the right complexity measure for their use-case.