10.1145/2556624.2556644acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicpsprocConference Proceedings
research-article

On lazy and eager interactive reconfiguration

ABSTRACT

An interactive configuration tool needs to provide feedback to the user on possible further decisions while respecting constraints of the product being configured. In the presence of a large number of product features, it reduces the configuration effort if users can start from a default configuration and adapt only those features that are important to them. Hence, rather than completing an empty configuration (empty product), it is easier to move from one complete configuration to another (from one product to another). This paper shows how to provide tool support for this approach to interactive configuration. Two types of algorithms, based on recent advancements in SAT technology, are introduced: lazy and eager. While the eager provides more information to the user, the lazy scales to configuration models with tens of thousands of features. This is confirmed by an experimental evaluation carried out with the implemented prototype.

References

  1. J. Amilhastre, H. Fargier, and P. Marquis. Consistency restoration and explanations in dynamic CSPs application to configuration. Artificial Intelligence, 135(1-2):199--234, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. N. Andersen, K. Czarnecki, S. She, and A. Wasowski. Efficient synthesis of feature models. In SPLC (1). ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Belov, M. Janota, I. Lynce, and J. Marques-Silva. On computing minimal equivalent subformulas. In CP. Springer, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Belov, I. Lynce, and J. Marques-Silva. Towards efficient MUS extraction. AI Comm., 25(2):97--116, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. H. K. Büning and O. Kullmann. Minimal unsatisfiability and autarkies. In Handbook of Satisfiability, volume 185. IOS Press, 2009.Google ScholarGoogle Scholar
  6. K. Czarnecki and A. Wasowski. Feature diagrams and logics: There and back again. In SPLC, pages 23--34. IEEE Computer Society, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. N. Eén and N. Sörensson. An extensible SAT-solver. In SAT, 2003.Google ScholarGoogle Scholar
  8. T. Hadzic, S. Subbarayan, R. M. Jensen, H. R. Andersen, J. Møller, and H. Hulgaard. Fast backtrack-free product configuration using a precompiled solution space representation. In PETO, 2004.Google ScholarGoogle Scholar
  9. E. Hebrard, B. Hnich, B. O'Sullivan, and T. Walsh. Finding diverse and similar solutions in constraint programming. In AAAI, pages 372--377. AAAI Press/The MIT Press, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. Janota. Do SAT solvers make good configurators? In SPLC (2), 2008.Google ScholarGoogle Scholar
  11. M. Janota. SAT Solving in Interactive Configuration. PhD thesis, University College Dublin, Nov. 2010.Google ScholarGoogle Scholar
  12. M. Janota, G. Botterweck, R. Grigore, and J. Marques-Silva. How to complete an interactive configuration process? In SOFSEM. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Kaiser and W. Küchlin. Detecting inadmissible and necessary variables in large propositional formulae. In Int. Joint Conf. on Aut. Reasoning: IJCAR, 2001.Google ScholarGoogle Scholar
  14. K. C. Kang, S. G. Cohen, J. A. Hess, W. E. Novak, and A. S. Peterson. Feature-oriented domain analysis (FODA) feasibility study, 1990.Google ScholarGoogle Scholar
  15. Linux variability analysis tools (LVAT). code.google.com/p/linux-variability-analysis-tools.Google ScholarGoogle Scholar
  16. J. Marques-Silva, F. Heras, M. Janota, A. Previti, and A. Belov. On computing minimal correction subsets. In IJCAI. IJCAI/AAAI, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. Marques-Silva, M. Janota, and A. Belov. Minimal sets over monotone predicates in boolean formulae. In CAV, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. Mendonça, A. Wasowski, and K. Czarnecki. SAT-based analysis of feature models is easy. In SPLC, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. Nöhrer, A. Biere, and A. Egyed. Managing SAT inconsistencies with HUMUS. In VaMoS. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. B. O'Callaghan, B. O'Sullivan, and E. C. Freuder. Generating corrective explanations for interactive constraint satisfaction. In CP. Springer, 2005.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. P.-Y. Schobbens, P. Heymans, J.-C. Trigaux, and Y. Bontemps. Generic semantics of feature diagrams. Computer Networks, 51(2):456--479, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. She, R. Lotufo, T. Berger, A. Wasowski, and K. Czarnecki. The variability model of the Linux kernel. In VaMoS, 2010.Google ScholarGoogle Scholar
  23. Software product lines online tools (SPLOT). http://www.splot-research.org/.Google ScholarGoogle Scholar
  24. T. Tanjo, N. Tamura, and M. Banbara. A compact and efficient SAT-encoding of finite domain CSP. In SAT, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. J. White, D. Benavides, D. C. Schmidt, P. Trinidad, B. Dougherty, and A. R. Cortés. Automated diagnosis of feature model configurations. Journal of Systems and Software, 83(7):1094--1107, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. On lazy and eager interactive reconfiguration

      Comments

      Login options

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

      Sign in

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!