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

TL;DR

The paper introduces Modeler Experience (MX) as a context-sensitive framework that aggregates UX, motivation, integration, collaboration & versioning, and language complexity to explain practitioner interactions with modeling tools. Through a qualitative study, it derives a three-tier MX taxonomy (Inherent, Technical, Non-Technical) and maps these factors to five modeling success stories: Infrastructure-as-Code, Low-code, MBSE, Informal, and Semi-formal modeling. Findings indicate MX factors vary across contexts, producing distinct trade-offs between language complexity, tooling capabilities, and organizational factors; MBSE demands high language maturity and tool support, while informal approaches favor lightweight tooling and collaboration. The work provides a foundation for human-centered tool design and workflow integration, with planned empirical validation, cross-domain analyses, and case studies to enhance adoption and productivity in model-driven engineering.

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.

Figures (1)

• Figure 1: MX factors and their relations