Change Patterns for Model Creation: Investigating the Role of Nesting Depth

TL;DR

This paper explores how nesting depth affects cognitive complexity when using change patterns for process model creation, proposing a research design to empirically test this impact.

Contribution

It introduces a research framework to investigate the influence of nesting depth on cognitive complexity in change pattern-based process modeling.

Findings

Nesting depth significantly impacts cognitive complexity.

Change pattern usage becomes more complex with increased nesting.

Proposed experiment to measure nesting depth effects.

Abstract

Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns offers a promising perspective. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM) based on change primitives has been investigated, little is known about this process based on change patterns and factors that impact the cognitive complexity of pattern usage. Insights from the field of cognitive psychology as well as observations from a pilot study suggest that the nesting depth of the model to be created has a significant impact on cognitive complexity. This paper proposes a research design to test the impact of nesting depth on the cognitive complexity of change pattern usage in an experiment.