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On the Challenges of Transforming UVL to IVML

  • Prankur Agarwal
  • , Kevin Feichtinger
  • , Klaus Schmid
  • , Holger Eichelberger
  • , Rick Rabiser

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

Abstract

Software product line techniques encourage the reuse and adaptation of software components for creating customized products or software systems. These different product variants have commonalities and differences, which are managed by variability modeling. Over the past three decades, both academia and industry have developed numerous variability modeling methods, each with its own advantages and disadvantages. Many of these methods have demonstrated their utility within specific domains or applications. However, comprehending the capabilities and differences among these approaches to pinpoint the most suitable one for a particular use case remains challenging. Thus, new modeling techniques and tailored tools for handling variability are frequently created. Transitioning between variability models through transformations from different approaches can help in understanding the benefits and drawbacks of different modeling approaches. However, implementing such transformations presents challenges, such as semantic preservation and avoiding information loss. TRAVART is a tool that helps with transitioning between different approaches by enabling the transformation of variability models into other variability models of different types. This paper discusses the challenges for such transformations between UVL and IVML. It also presents a one-way transformation from the UVL to IVML with as little information loss as possible.
Original languageEnglish
Title of host publicationOn the Challenges of Transforming UVL to IVML
PublisherarXiv
Pages1-5
Number of pages5
Volumeabs/2403.01952
DOIs
Publication statusPublished - Mar 2024

Publication series

NameCoRR

Fields of science

  • 102022 Software development
  • 102025 Distributed systems
  • 102029 Practical computer science

JKU Focus areas

  • Digital Transformation

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