A Flexible Approach for Transforming Variability Models

  • Kevin Feichtinger

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

In software product lines, engineers use variability models to explicitly represent commonalities and variability. A plethora of variability modeling approaches have been proposed in the last 30 years, and there is no standard variability modeling approach the community agrees on. Well-known approaches such as feature modeling or decision modeling exist in many different variants, most of which have been shown to be useful for at least one specific use case. Due to this variety of approaches researchers and practitioners alike struggle to find, understand, and eventually pick the right approach for a specific context or (set of) system(s). Practitioners in industry often develop custom solutions to manage the variability of various artifacts, like requirements documents or design spreadsheets. In this paper, we report on our ongoing research towards developing a framework for (semi-)automatically transforming variability models. Our approach supports researchers and practitioners experimenting with and comparing different variability modeling approaches and switching from one modeling approach to another. We present the research questions guiding our research and discuss the current status of our work as well as future work.
Original languageEnglish
Title of host publicationProc. of the 25th International Systems and Software Product Line Conference (SPLC 2021) vol. 2
EditorsMohammad Mousavi, Pierre-Yves Schobbens, Hugo Araujo, Ina Schaefer, Maurice H. ter Beek, Xavier Devroey, Jose Miguel Rojas, Rick Rabiser, Mahsa Varshosaz, Tomoji Kishi, Jaejoon Lee
Place of PublicationNew York, USA
PublisherACM
Pages18-23
Number of pages6
ISBN (Electronic)9781450384704
ISBN (Print)978-1-4503-8470-4
DOIs
Publication statusPublished - 06 Sept 2021

Publication series

NameACM International Conference Proceeding Series
VolumePart F171625-B

Fields of science

  • 102 Computer Sciences
  • 102022 Software development
  • 102029 Practical computer science

JKU Focus areas

  • Digital Transformation

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