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Modeling variability in the video domain: language and experience report
Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, 2 Ave JF Kennedy, L-1855 Luxembourg, Luxembourg..
Univ Rennes, DiverSE Team Inria Rennes, IRISA, CNRS, Rennes, France..
Univ Seville, Dept Comp Languages & Syst, Seville, Spain..
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0002-4015-4640
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2019 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 27, no 1, p. 307-347Article in journal (Refereed) Published
Abstract [en]

In an industrial project, we addressed the challenge of developing a software-based video generator such that consumers and providers of video processing algorithms can benchmark them on a wide range of video variants. This article aims to report on our positive experience in modeling, controlling, and implementing software variability in the video domain. We describe how we have designed and developed a variability modeling language, called VM, resulting from the close collaboration with industrial partners during 2 years. We expose the specific requirements and advanced variability constructs; we developed and used to characterize and derive variations of video sequences. The results of our experiments and industrial experience show that our solution is effective to model complex variability information and supports the synthesis of hundreds of realistic video variants. From the software language perspective, we learned that basic variability mechanisms are useful but not enough; attributes and multi-features are of prior importance; meta-information and specific constructs are relevant for scalable and purposeful reasoning over variability models. From the video domain and software perspective, we report on the practical benefits of a variability approach. With more automation and control, practitioners can now envision benchmarking video algorithms over large, diverse, controlled, yet realistic datasets (videos that mimic real recorded videos)-something impossible at the beginning of the project.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 27, no 1, p. 307-347
Keywords [en]
Variability modeling, Feature modeling, Software product line engineering, Configuration, Automated reasoning, Domain-specific languages, Video testing
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-249887DOI: 10.1007/s11219-017-9400-8ISI: 000462236000009Scopus ID: 2-s2.0-85043359422OAI: oai:DiVA.org:kth-249887DiVA, id: diva2:1307248
Note

QC 20190426

Available from: 2019-04-26 Created: 2019-04-26 Last updated: 2019-04-26Bibliographically approved

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Baudry, Benoit

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  • apa
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More languages
Output format
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