skip to main content
10.1145/2430502.2430513acmotherconferencesArticle/Chapter ViewAbstractPublication PagesvamosConference Proceedingsconference-collections
research-article

A survey of variability modeling in industrial practice

Published:23 January 2013Publication History

ABSTRACT

Over more than two decades, numerous variability modeling techniques have been introduced in academia and industry. However, little is known about the actual use of these techniques. While dozens of experience reports on software product line engineering exist, only very few focus on variability modeling. This lack of empirical data threatens the validity of existing techniques, and hinders their improvement. As part of our effort to improve empirical understanding of variability modeling, we present the results of a survey questionnaire distributed to industrial practitioners. These results provide insights into application scenarios and perceived benefits of variability modeling, the notations and tools used, the scale of industrial models, and experienced challenges and mitigation strategies.

References

  1. A. Abele, R. Johansson, H. Lönn, Y. Papadopoulos, M. Reiser, D. Servat, M. Törngren, and M. Weber. The CVM framework: A prototype tool for compositional variability management. In VaMoS'10, 2010.Google ScholarGoogle Scholar
  2. A. Aldazabal and S. Erofeev. Product line unified modeler (PLUM). 2007.Google ScholarGoogle Scholar
  3. T. Berger and S. She. Formal semantics of the CDL language, 2010. Technical Note. Available at http://thorsten-berger.net/cdl_semantics.pdf.Google ScholarGoogle Scholar
  4. T. Berger, S. She, R. Lotufo, A. Wąsowski, and K. Czarnecki. Variability modeling in the real: A perspective from the operating systems domain. In ASE'10, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. T. Berger, S. She, R. Lotufo, A. Wąsowski, and K. Czarnecki. Variability modeling in the systems software domain. Technical Report GSDLAB-TR 2012-07-06, Generative Software Development Laboratory, University of Waterloo, 2012. Available at http://gsd.uwaterloo.ca/tr/vm-2012-berger.Google ScholarGoogle Scholar
  6. A. Birk. Product line engineering, the state of the practice. IEEE Software, 20(6):52--60, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. R. Bonifácio, L. Teixeira, and P. Borba. Hephaestus: A tool for managing spl variabilities. In SBCARS Tools Session, 2009.Google ScholarGoogle Scholar
  8. T. Browning. Applying the design structure matrix to system decomposition and integration problems: a review and new directions. IEEE Transaction on Engineering Management, 48(3):292--306, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  9. L. Chen and M. Ali Babar. Variability management in software product lines: an investigation of contemporary industrial challenges. In SPLC'10, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. L. Chen, M. Ali Babar, and N. Ali. Variability management in software product lines: a systematic review. In SPLC'09, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. L. Chen and M. A. Babar. A systematic review of evaluation of variability management approaches in software product lines. Information and Software Technology, 53(4):344--362, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. CVL Submission Team. Common variability language (CVL), OMG revised submission, 2012. Available at http://www.omgwiki.org/variability/lib/exe/fetch.php?id=start&cache=cache&media=cvl-revised-submission.pdf.Google ScholarGoogle Scholar
  13. K. Czarnecki, P. Grünbacher, R. Rabiser, K. Schmid, and A. Wąsowski. Cool features and tough decisions: A comparison of variability modeling approaches. In VAMOS'12, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. O. Djebbi, C. Salinesi, and G. Fanmuy. Industry survey of product lines management tools: Requirements, qualities and open issues. In RE'07, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  15. R. Flores, C. Krueger, and P. Clements. Mega-scale product line engineering at general motors. In SPLC'12, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. C. Gillan, P. Kilpatrick, I. Spence, T. Brown, R. Bashroush, R. Gawley, et al. Challenges in the application of feature modelling in fixed line telecommunications. In VaMoS'07, 2007.Google ScholarGoogle Scholar
  17. P. Grünbacher, R. Rabiser, D. Dhungana, and M. Lehofer. Model-based customization and deployment of Eclipse-based tools: Industrial experiences. In ASE'09, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. P. Heymans and J.-C. Trigauxf. Software product line: State of the art. Technical Report EPH3310300R0462/215315, Institut d'Informatique FUNDP, Namur, 2003. Available at http://www.inf.ufpr.br/silvia/topicos/artigostrab10/artigo1-S1e2.pdf.Google ScholarGoogle Scholar
  19. A. Hubaux, A. Classen, M. Mendonça, and P. Heymans. A preliminary review on the application of feature diagrams in practice. In VaMoS'10, 2010.Google ScholarGoogle Scholar
  20. A. Hubaux, Y. Xiong, and K. Czarnecki. A user survey of configuration challenges in linux and ecos. In VaMoS'12, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. H. P. Jepsen and D. Beuche. Running a software product line: standing still is going backwards. In SPLC'09, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. H. Khandkar. Open coding. Lecture material, available at http://pages.cpsc.ucalgary.ca/~saul/wiki/uploads/CPSC681/open-coding.pdf, 2009.Google ScholarGoogle Scholar
  23. C. W. Krueger. Easing the transition to software mass customization. In PFE'01, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. C. W. Krueger. New methods in software product line development. In SPLC'06, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. R. Lotufo, S. She, T. Berger, K. Czarnecki, and A. Wąsowski. Evolution of the Linux kernel variability model. In SPLC'10, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. S. Mann and G. Rock. Dealing with variability in architecture de-scriptions to support automotive product lines. In VaMoS'09, 2009.Google ScholarGoogle Scholar
  27. M. Mendonça, A. Wąsowski, and K. Czarnecki. SAT-based analysis of feature models is easy. In SPLC'09, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. R. Rabiser, P. Grünbacher, and D. Dhungana. Requirements for product derivation support: Results from a systematic literature review and an expert survey. Information and Software Technology, 52(3), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. J. Refstrup. Adapting to change: Architecture, processes and tools: A closer look at HP's experience in evolving the owen software product line. In SPLC'09, 2009. Keynote, available at http://www.sei.cmu.edu/splc2009/files/SPLC2009AdoptingtoChange_Owen_2009_final.pdf.Google ScholarGoogle Scholar
  30. M. Reiser, R. Tavakoli, and M. Weber. Unified feature modeling as a basis for managing complex system families. In VaMoS'07, 2007.Google ScholarGoogle Scholar
  31. M. Riebisch, D. Streitferdt, and I. Pashov. Modeling variability for object-oriented product lines. In ECOOP'03 Workshop Reader. 2004.Google ScholarGoogle ScholarCross RefCross Ref
  32. J. Schroeter, S. Cech, S. Götz, C. Wilke, and U. Assmann. Towards modeling a variable architecture for multi-tenant SaaS-applications. In VaMoS'12, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. C. Schwanninger, I. Groher, C. Elsner, and M. Lehofer. Variability modelling throughout the product line lifecycle. In MODELS'09, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. S. She and T. Berger. Formal semantics of the Kconfig language, 2010. Technical Note. Available at http://eng.uwaterloo.ca/~shshe/kconfig_semantics.pdf.Google ScholarGoogle Scholar
  35. S. She, R. Lotufo, T. Berger, A. Wąsowski, and K. Czarnecki. The variability model of the Linux kernel. In VaMoS'10, 2010.Google ScholarGoogle Scholar
  36. M. Sinnema and S. Deelstra. Classifying variability modeling techniques. Information and Software Technology, 49(7):717--739, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Software Engineering Institute. Catalog of software product lines. http://www.sei.cmu.edu/productlines/casestudies/catalog/index.cfm.Google ScholarGoogle Scholar
  38. M. Steger, C. Tischer, B. Boss, A. Müller, O. Pertler, W. Stolz, and S. Ferber. Introducing PLA at bosch gasoline systems: Experiences and practices. In SPLC'04, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  39. R. Stoiber and M. Glinz. Modeling and managing tacit product line requirements knowledge. In MARK'09, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. R. Tartler, D. Lohmann, J. Sincero, and W. Schröder-Preikschat. Feature consistency in compile-time-configurable system software: facing the linux 10,000 feature problem. In EuroSys'11, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. C. Thörn. Current state and potential of variability management practices in software-intensive SMEs: Results from a regional industrial survey. Information and Software Technology, 52(4):411--421, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. F. J. van der Linden, K. Schmid, and E. Rommes. Software Product Lines in Action: The Best Industrial Practice in Product Line Engineering. Springer, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A survey of variability modeling in industrial practice

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          VaMoS '13: Proceedings of the 7th International Workshop on Variability Modelling of Software-Intensive Systems
          January 2013
          136 pages
          ISBN:9781450315418
          DOI:10.1145/2430502

          Copyright © 2013 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 23 January 2013

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate66of147submissions,45%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader