Systematizing Modeler Experience (MX) in Model-Driven Engineering Success Stories

TL;DR

This paper investigates how modeler experience influences the adoption and effectiveness of modeling tools in model-driven engineering, emphasizing factors like usability, motivation, and collaboration.

Contribution

It systematically analyzes the factors affecting modeler experience and their impact on modeling practices, providing insights for improving modeling tool design.

Findings

MX factors significantly influence modeling tool adoption

User experience correlates with motivation and collaboration

Considering MX improves understanding of modeling tool usage

Abstract

Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of ``modeler experience'' (MX). MX includes factors such as user experience, motivation, integration, collaboration \& versioning and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling.