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2022 | OriginalPaper | Buchkapitel

Assisted-Modeling Requirements for Model-Driven Development Tools

verfasst von : David Mosquera, Marcela Ruiz, Oscar Pastor, Jürgen Spielberger

Erschienen in: Research Challenges in Information Science

Verlag: Springer International Publishing

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Abstract

Model-driven development (MDD) tools allow software development teams to increase productivity and decrease software time-to-market. Although several MDD tools have been proposed, they are not commonly adopted by software development practitioners. Some authors have noted MDD tools are poorly adopted due to a lack of user assistance during modeling-related tasks. This has led model-driven engineers—i.e., engineers who create MDD tools—to equip MDD tools with intelligent assistants, wizards for creating models, consistency checkers, and other modeling assistants to address such assist-modeling-related issues. However, is this the way MDD users expect to be assisted during modeling in MDD tools? Therefore, we plan and conduct two focus groups with MDD users. We extract data around three main research questions: i) what are the challenges perceived by MDD users during modeling for later code generation? ii) what are the features of the current modeling assistants that users like/dislike? and iii) what are the user’s needs that are not yet satisfied by the current modeling assistants? As a result, we gather requirements from the MDD users’ perspective on how they would like to be assisted while using MDD tools. We propose an emerging framework for assisting MDD users during modeling based on such requirements. In addition, we outline future challenges and research efforts for next-generation MDD tools.

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Fußnoten
1
Hereafter, we use the term “subject/subjects,” referring to the focus group subject/subjects that fits/fit in one of the established MDD user types.
 
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Metadaten
Titel
Assisted-Modeling Requirements for Model-Driven Development Tools
verfasst von
David Mosquera
Marcela Ruiz
Oscar Pastor
Jürgen Spielberger
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-031-05760-1_27