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Order Matters: A UVL Extension for Configuration Sequences

Meixner, Kristof ; Feichtinger, Kevin ORCID iD icon 1; Laglstorfer, Robert; Greiner, Sandra
1 Institut für Informationssicherheit und Verlässlichkeit (KASTEL), Karlsruher Institut für Technologie (KIT)

Abstract:

Feature models are widely used for representing the variability of (software) systems. Existing feature model configuration tools typically display all selectable features at once. This limits their suitability for use cases that require capturing the order of selected features, or for cases where configuration benefits from gradually revealing the selectable features. Examples include guided product configuration, production process configuration, or DNA sequence synthesis. In this tool paper, we implement visibility constraints for feature models to explicitly specify conditions for when features become visible during configuration. We integrate these visibility constraints into the Universal Variability Language and offer an extension to FeatureIDE to allow modeling configuration flows while executing them within a single variability model. We demonstrate the tool’s feasibility by applying visibility constraints in two use cases. This illustrates how visibility constraints can support ordered configuration without relying on auxiliary feature hierarchies or tool-specific workarounds.


Originalveröffentlichung
DOI: 10.1007/978-3-032-40421-3_8
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2027
Sprache Englisch
Identifikator ISBN: 978-3-032-40421-3
ISSN: 0302-9743
KITopen-ID: 1000198369
Erschienen in Software and Systems Reuse, Product Lines, and Configuration -- VARIABILITY 2026 Satellite Events – Limassol, Cyprus, September 29–October 2, 2026, Proceedings. Ed.: G. Perrouin
Veranstaltung International Conference on Software and Systems Reuse, Product Lines, and Configuration (VARIABILITY 2026), Limassol, Zypern, 29.09.2026 – 02.10.2026
Verlag Springer Nature Switzerland
Seiten 94–103
Serie Lecture Notes in Computer Science
Projektinformation SFB 1608/1, 501798263 (DFG, DFG KOORD, SFB 1608)
Vorab online veröffentlicht am 10.10.2026
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