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Finding a Universal Execution Strategy for Model Transformation Networks

Gleitze, Joshua; Klare, Heiko; Burger, Erik ORCID iD icon

Abstract (englisch):

When using multiple models to describe a (software) system, one can use a network of model transformations to keep the models consistent after changes. No strategy exists, however, to orchestrate the execution of transformations if the network has an arbitrary topology. In this paper, we analyse how often and in which order transformations need to be executed. We argue why linear execution bounds are too restrictive to be useful in practice and prove that there is no upper bound for the number of necessary executions. To avoid non-termination, we propose a conservative strategy that makes execution failures easier to understand. These insights help developers and users of transformation networks to understand under which circumstances their networks can terminate. Additionally, the proposed strategy helps them to find the cause when a network cannot restore consistency.


Verlagsausgabe §
DOI: 10.5445/IR/1000132312
Veröffentlicht am 24.11.2021
Originalveröffentlichung
DOI: 10.1007/978-3-030-71500-7_5
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Institut für Programmstrukturen und Datenorganisation (IPD)
Kompetenzzentrum für angewandte Sicherheitstechnologie (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISBN: 978-3-030-71500-7
ISSN: 0302-9743, 1611-3349
KITopen-ID: 1000132312
HGF-Programm 46.23.03 (POF IV, LK 01) Engineering Security for Mobility Systems
Erschienen in Fundamental Approaches to Software Engineering. Ed.: E. Guerra
Veranstaltung 24th International Conference on Fundamental Approaches to Software Engineering (FASE/ETAPS 2021), Online, 27.03.2021 – 01.04.2021
Verlag Springer International Publishing
Seiten 87–107
Serie Lecture Notes in Computer Science ; 12649
Vorab online veröffentlicht am 24.02.2021
Schlagwörter model consistency; model transformation networks
Nachgewiesen in Dimensions
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