Propagating Decisions to Detect and Explain Conflicts in a Multi-step Configuration Process

  • Jaime Chavarriaga
  • Carlos Noguera
  • Rubby Casallas
  • Viviane Jonckers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8767)


In configuration processes with multiple stakeholders, conflicts are very likely because each decision maker has a different concerns and expectations about the product. They may not be aware of features selected by others or the restrictions that these selections impose. To help solve the conflicts, this paper introduces a new approach to provide explanations about their causes. Our approach is based on representing features from different concerns using different Feature Models (FMs), and relating them through Feature-Solution Graphs. An FSG contains dependency relationships between two FMs: one feature from the left side forces or prohibits the selection of features in the right side feature model. The strategy to detect and explain conflicts is based on propagation of constraints over the FSGs. We claim that our approach is more expressive and efficient than when using a single FM that contains all concerns and SAT solvers to detect conflicts.


Multi-level configuration processes Feature Models Feature-Solution Graphs Conflict explanation 


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  1. 1.
    Kang, K.C., Cohen, S.G., Hess, J.A., Novak, W.E., Peterson, A.S.: Feature-Oriented Domain Analysis (FODA) feasibility study (CMU/SEI-90-TR-021). Technical report, Software Engineering Institute, Carnegie Mellon University (1990)Google Scholar
  2. 2.
    Czarnecki, K., Helsen, S., Eisenecker, U.: Staged Configuration through Specialization and Multilevel Configuration of Feature Models. Software Process: Improvement and Practice 10(2), 143–169 (2005)CrossRefGoogle Scholar
  3. 3.
    Hubaux, A., Tun, T.T., Heymans, P.: Separation of Concerns in Feature Diagram languages: A Systematic Survey. ACM Computing Surveys 45(4), 1–23 (2013)CrossRefGoogle Scholar
  4. 4.
    White, J., Schmidt, D., Benavides, D., Trinidad, P., Ruiz-Cortes, A.: Automated Diagnosis of Product-Line Configuration Errors in Feature Models. In: 12th International Software Product Line Conference (SPLC 2008), pp. 225–234 (2008)Google Scholar
  5. 5.
    Janota, M.: SAT Solving in Interactive Configuration. PhD thesis, University College of Dublin (2010)Google Scholar
  6. 6.
    Nöhrer, A., Biere, A., Egyed, A.: Managing SAT inconsistencies with HUMUS. In: Proceedings of the Sixth International Workshop on Variability Modelling of Software-Intensive Systems, Leipzig, Germany, January 25-27, pp. 83–91. ACM (2012)Google Scholar
  7. 7.
    Nöhrer, A., Biere, A., Egyed, A.: A Comparison of Strategies for Tolerating Inconsistencies during Decision-Making. In: 16th International Software Product Line Conference (SPLC 2012), pp. 11–20. ACM (2012)Google Scholar
  8. 8.
    Chavarriaga, J., Noguera, C., Casallas, R., Jonckers, V.: Supporting Multi-Level Configuration with Feature-Solution Graphs: Formal Semantics and Alloy implementation. Technical report, Vrije Universiteit Brussel (2013)Google Scholar
  9. 9.
    Bass, L., Clements, P., Kazman, R.: Software Architecture in Practice. Addison-Wesley Professional (2012)Google Scholar
  10. 10.
    Chavarriaga, J., Noguera, C., Casallas, R., Jonckers, V.: Architectural Tactics support in Cloud Computing Providers: the Jelastic case. In: Proceedings of the International ACM Sigsoft Conference on the Quality of Software Architectures, QoSA 2014 (2014)Google Scholar
  11. 11.
    Classen, A., Hubaux, A., Heymans, P.: A Formal Semantics for Multi-Level Staged Configuration. In: Benavides, D., Metzger, A., Eisenecker, U.W. (eds.) Third International Workshop on Variability Modelling of Software-Intensive Systems (VaMoS 2009). ICB Research Report, vol. 29, pp. 51–60. Universität Duisburg-Essen (2009)Google Scholar
  12. 12.
    Hubaux, A., Heymans, P., Schobbens, P.Y., Deridder, D., Abbasi, E.: Supporting Multiple Perspectives in Feature-Based Configuration. Software and Systems Modeling (SoSyM), 1–23 (2011)Google Scholar
  13. 13.
    de Bruijn, H., van Vliet, H.: Scenario-based Generation and Evaluation of Software Architectures. In: Dannenberg, R.B. (ed.) GCSE 2001. LNCS, vol. 2186, pp. 128–139. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Janota, M., Botterweck, G.: Formal Approach to integrating Feature and Architecture Models. In: Fiadeiro, J.L., Inverardi, P. (eds.) FASE 2008. LNCS, vol. 4961, pp. 31–45. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Benavides, D., Segura, S., Trinidad, P., Ruiz-Cortés, A.: FAMA: Tooling a framework for the automated analysis of Feature Models. In: First International Workshop on Variability Modelling of Software-intensive Systems, VAMOS (2007)Google Scholar
  16. 16.
    Pohl, R., Lauenroth, K., Pohl, K.: A Performance Comparison of Contemporary Algorithmic Approaches for Automated Analysis Operations on Feature Models. In: Proceeedings of the 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), pp. 313–322. IEEE (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jaime Chavarriaga
    • 1
    • 2
  • Carlos Noguera
    • 2
  • Rubby Casallas
    • 1
  • Viviane Jonckers
    • 2
  1. 1.Universidad de los AndesColombia
  2. 2.Vrije Universiteit BrusselBelgium

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