怎样才能做出好的建模研究贡献？

IF 2 3区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software and Systems Modeling Pub Date : 2024-04-29 DOI:10.1007/s10270-024-01177-x

Richard F. Paige, Jordi Cabot

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引用次数: 0

摘要

建模领域正在迅速发展和扩大，以解决新的研究课题并与新的学科建立联系。因此，十年前构成优秀建模研究论文的要素今天可能已经不同。我们试图提炼出我们（以及我们旨在代表的社区）认为当今软件和系统建模领域优秀研究论文的关键要素的一些见解。这些见解不仅对新论文的作者有用，而且对审稿人和编辑也有帮助。

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What makes a good modeling research contribution?

The modeling field is rapidly evolving and expanding to address new research topics and to connect with new disciplines. As such, what constituted a good modeling research contribution ten years ago may not be the same today. We try to distill some insights of what we (and the community we aim to represent) consider today as key elements of a good research paper in the field of software and systems modeling. Such insights—which will need to evolve and adapt with time—will be useful not just for authors of new papers, but also for reviewers and editors.

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来源期刊

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