Models@run.time:a Guided Tour of the State-of-the-Art and Research Challenges


More than a decade ago, the research topic models@run.time was coined. Since then, the research area has received increasing attention. Given the prolific results during these years, the current outcomes need to be sorted and classified. Furthermore, many gaps need to be categorized in order to further develop the research topic by experts of the research area but also newcomers. Accordingly, the paper discusses the principles and requirements of models@run.time and the state of the art of the research line. To make the discussion more concrete, a taxonomy is defined and used to compare the main approaches and research outcomes in the area during the last decade and including ancestor research initiatives. We identified and classified 275 papers on models@run.time, which allowed us to identify the underlying research gaps and to elaborate on the corresponding research challenges. Finally, we also facilitate sustainability of the survey over time by offering tool support to add, correct and visualize data.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Uncontrolled Keywords: Causal connection,Models@run.time,Self-reflection,Systematic literature review,Software,Modelling and Simulation
Publication ISSN: 1619-1374
Full Text Link:
Related URLs: https://link.sp ... 270-018-00712-x (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2019-10-01
Published Online Date: 2019-01-09
Accepted Date: 2018-07-22
Authors: Bencomo, Nelly (ORCID Profile 0000-0001-6895-1636)
Götz, Sebatian
Song, Hui



Version: Accepted Version

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