Complexity metrics for DMN decision models

Abstract

Complexity impairs the maintainability and understandability of conceptual models. Complexity metrics have been used in software engineering and business process management (BPM) to capture the degree of complexity of conceptual models. The recent introduction of the Decision Model and Notation (DMN) standard provides opportunities to shift towards the Separation of Concerns paradigm when it comes to modelling processes and decisions. However, unlike for processes, no studies exist that address the representational complexity of DMN decision models. In this paper, we provide an initial set of complexity metrics for DMN models. We gather insights from the process modelling and software engineering fields to propose complexity metrics for DMN decision models. Additionally, we provide an empirical complexity assessment of DMN decision models. For the decision requirements level of the DMN standard 19 metrics were proposed, while 7 metrics were put forward for the decision logic level. For decision requirements, the model size-based metrics, the Durfee Square Metric (DSM) and the Perfect Square Metric (PSM) prove to be the most suitable. For the decision logic level of DMN the Hit Policy Usage (HPU) and the Total Number of Input Variables (TNIV) were evaluated as suitable for measuring DMN decision table complexity.