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Configuring Software Product Line Feature Models Based on Stakeholders’ Soft and Hard Requirements

  • Ebrahim Bagheri
  • Tommaso Di Noia
  • Azzurra Ragone
  • Dragan Gasevic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6287)

Abstract

Feature modeling is a technique for capturing commonality and variability. Feature models symbolize a representation of the possible application configuration space, and can be customized based on specific domain requirements and stakeholder goals. Most feature model configuration processes neglect the need to have a holistic approach towards the integration and satisfaction of the stakeholder’s soft and hard constraints, and the application-domain integrity constraints. In this paper, we will show how the structure and constraints of a feature model can be modeled uniformly through Propositional Logic extended with concrete domains, called \(\mathcal{P}{(N)}\). Furthermore, we formalize the representation of soft constraints in fuzzy \(\mathcal{P}{(N)}\) and explain how semi-automated feature model configuration is performed. The model configuration derivation process that we propose respects the soundness and completeness properties.

Keywords

Feature Model Product Family Constraint Satisfaction Problem Integrity Constraint Soft Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Pohl, K., Böckle, G., Van Der Linden, F.: Software Product Line Engineering: Foundations, Principles, and Techniques. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  2. 2.
    Czarnecki, K., Helsen, S., Eisenecker, U.: Staged configuration using feature models. In: Nord, R.L. (ed.) SPLC 2004. LNCS, vol. 3154, pp. 266–283. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Kang, K., Lee, J., Donohoe, P.: Feature-oriented product line engineering. IEEE Software 19, 58–65 (2002)CrossRefGoogle Scholar
  4. 4.
    Lopez-Herrejon, R., Batory, D.: A standard problem for evaluating product-line methodologies. In: Bosch, J. (ed.) GCSE 2001. LNCS, vol. 2186, pp. 10–24. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Mendonca, M., Wasowski, A., Czarnecki, K., Cowan, D.: Efficient compilation techniques for large scale feature models. In: International Conference on GPCE, pp. 13–22 (2008)Google Scholar
  6. 6.
    Wang, H., Li, Y., Sun, J., Zhang, H., Pan, J.: Verifying feature models using OWL. Web Semantics: Science, Services and Agents on the World Wide Web 5, 117–129 (2007)CrossRefGoogle Scholar
  7. 7.
    Batory, D.: Feature models, grammars, and propositional formulas. In: Obbink, H., Pohl, K. (eds.) SPLC 2005. LNCS, vol. 3714, pp. 7–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Ragone, A., Noia, T.D., Sciascio, E.D., Donini, F.M.: Logic-based automated multi-issue bilateral negotiation in peer-to-peer e-marketplaces. JAAMAS 16, 249–270 (2008)Google Scholar
  9. 9.
    Sommerville, I., Sawyer, P.: Viewpoints: principles, problems and a practical approach to requirements engineering. Annals of Software Engineering 3, 101–130 (1997)CrossRefGoogle Scholar
  10. 10.
    Ausiello, G., Crescenzi, P., Kann, V., Marchetti-Sp, Gambosi, G., Spaccamela, A.M.: Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties (2003)Google Scholar
  11. 11.
    Papadimitriou, C., Steiglitz, K.: Combinatorial Optimization: algorithms and Complexity. Prentice-Hall, Inc., Englewood Cliffs (1982)zbMATHGoogle Scholar
  12. 12.
    Mamdani, E.: Application of fuzzy logic to approximate reasoning using linguistic synthesis. In: Sixth International Symposium on Multiple-Valued Logic, pp. 196–202 (1976)Google Scholar
  13. 13.
    Yager, R., Filev, D.: Essentials of fuzzy modeling and control. John Wiley, Chichester (1994)Google Scholar
  14. 14.
    Schobbens, P., Heymans, P., Trigaux, J.: Feature diagrams: A survey and a formal semantics. In: 14th IEEE International Conference Requirements Engineering, pp. 139–148 (2006)Google Scholar
  15. 15.
    Janota, M., Kiniry, J.: Reasoning about feature models in higher-order logic. In: Software Product Line Conference 2007, pp. 13–22 (2007)Google Scholar
  16. 16.
    Benavides, D., Segura, S., Trinidad, P., Ruiz-Cortes, A.: FAMA: Tooling a framework for the automated analysis of feature models. In: VAMOS Workshop, pp. 129–134 (2007)Google Scholar
  17. 17.
    Benavides, D., Trinidad, P., Ruiz-Cortes, A.: Automated reasoning on feature models. In: Pastor, Ó., Falcão e Cunha, J. (eds.) CAiSE 2005. LNCS, vol. 3520, pp. 491–503. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Batory, D.: Feature models, grammars, and propositional formulas. In: Obbink, H., Pohl, K. (eds.) SPLC 2005. LNCS, vol. 3714, pp. 7–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Benavides, D., Segura, S., Trinidad, P., Ruiz-Cortés, A.: A first step towards a framework for the automated analysis of feature models. Technical Report (2006)Google Scholar
  20. 20.
    Jackson, D.: Alloy: a lightweight object modelling notation. ACM Trans. Softw. Eng. Methodol. 11, 256–290 (2002)CrossRefGoogle Scholar
  21. 21.
    Gheyi, R., Massoni, T., Borba, P.: A theory for feature models in alloy. In: First Alloy Workshop, pp. 71–80 (2006)Google Scholar
  22. 22.
    Czarnecki, K., She, S., Wasowski, A.: Sample spaces and feature models: There and back again. In: SPLC 2008, pp. 22–31. IEEE Computer Society, Washington (2008)Google Scholar
  23. 23.
    Robak, S., Pieczynski, A.: Employing fuzzy logic in feature diagrams to model variability in software product-lines. In: ECBS 2003, pp. 305–311 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ebrahim Bagheri
    • 1
  • Tommaso Di Noia
    • 1
  • Azzurra Ragone
    • 1
  • Dragan Gasevic
    • 1
  1. 1.NRC-IIT, Politecnico di BariUniversity of Trento, Athabasca University 

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