Systematizing modeler experience (MX) in model-driven engineering success stories

IF 2 3区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software and Systems Modeling Pub Date : 2024-07-11 DOI:10.1007/s10270-024-01194-w
Reyhaneh Kalantari, Julian Oertel, Joeri Exelmans, Satrio Adi Rukmono, Vasco Amaral, Matthias Tichy, Katharina Juhnke, Jan-Philipp Steghöfer, Silvia Abrahão
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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 and 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.

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将模型驱动工程成功案例中的建模人员经验(MX)系统化
建模通常与复杂和繁重的工具相关联,导致从业人员的负面看法。然而,其他范式,如一切皆代码或低代码,因其易于使用而逐渐被接受。本文从 "建模者体验"(MX)的角度探讨了这些看法之间的对立。MX 包括用户体验、动机、集成、协作和版本管理以及语言复杂性等因素。我们研究了这些因素之间的关系及其对不同建模使用场景的影响。我们的研究结果强调了在了解开发人员如何与建模工具互动以及建模和相关工具的复杂性时考虑 MX 的重要性。
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来源期刊
Software and Systems Modeling
Software and Systems Modeling 工程技术-计算机:软件工程
CiteScore
6.00
自引率
20.00%
发文量
104
审稿时长
>12 weeks
期刊介绍: We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns: Domain-specific models and modeling standards; Model-based testing techniques; Model-based simulation techniques; Formal syntax and semantics of modeling languages such as the UML; Rigorous model-based analysis; Model composition, refinement and transformation; Software Language Engineering; Modeling Languages in Science and Engineering; Language Adaptation and Composition; Metamodeling techniques; Measuring quality of models and languages; Ontological approaches to model engineering; Generating test and code artifacts from models; Model synthesis; Methodology; Model development tool environments; Modeling Cyberphysical Systems; Data intensive modeling; Derivation of explicit models from data; Case studies and experience reports with significant modeling lessons learned; Comparative analyses of modeling languages and techniques; Scientific assessment of modeling practices
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