A Multimodeling Approach for Quality-Driven Architecture Derivation

  • Emilio InsfranEmail author
  • Silvia Abrahão
  • Javier González-Huerta
  • John D. McGregor
  • Isidro Ramos
Conference paper


Product architecture derivation is a crucial activity in software product line (SPL) development since an inadequate decision during the architecture design directly impacts the quality of the product under development. Although some methods for architecture derivation have been proposed in the last few years, there is still a need for approaches that model the impact among architectural design decisions and quality attributes and use this information to drive the derivation of high-quality product architectures. In this paper, we present an approach for integrating quality attributes in early stages of the SPL lifecycle. The approach is based on a multimodel that explicitly represents the product line from multiple viewpoints (e.g., variability, functional, and quality) and the relationships among them, as well as on a derivation process that makes use of this multimodel to derive a product architecture with the required quality attributes from the product line architecture. The feasibility of the approach is illustrated using a case study on the automotive domain.


Quality Attribute Software Product Line Fault Tree Product Architecture Architectural Pattern 
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.



This research is supported by the MULTIPLE project (MICINN TIN2009-13838), the ValI+D fellowship program (ACIF/2011/235) and the Universitat Politècnica de València PAID program (PAID-00-12).


  1. 1.
    Barkmeyer EJ, Feeney AB, Denno P, Flater DW, Libes DE, Steves MP, Wallace EK (2003) Concepts for automating systems integration. NISTIR 6928, National Institute of Standards and Technology, U.S. Department of Commerce, USAGoogle Scholar
  2. 2.
    Botterweck G, O’Brien L, Thiel S (2007) Model-driven derivation of product architectures. Proceedings of the ASE 2007 conference. ACM, New York, USA, pp 469–472Google Scholar
  3. 3.
    Bosch J (2000) Design and use of software architectures. Adopting and evolving product-line approach. Addison-Wesley, HarlowGoogle Scholar
  4. 4.
    Cabello ME, Ramos I, Gómez A, Limón R (2009) Baseline-oriented modeling: an mda approach based on software product lines for the expert systems development. Conference on intelligent information and database systems, Dong hoi, Vietnam, pp 208–213Google Scholar
  5. 5.
    Czarnecki K, Kim CH (2005) Cardinality-based feature modeling and constraints: a progress report. Proceedings of the international workshop on software factories, San Diego, CA, USAGoogle Scholar
  6. 6.
    Clements P, Bachmann F, Bass L, Garlan D, Ivers J, Little R, Merson P, Nord R, Stafford J (2010) Documenting software architectures: views and beyond. Addison-Wesley, Boston, MAGoogle Scholar
  7. 7.
    Clements P, Northrop L (2007) Software product lines: practices and patterns. Addison-Wesley, Boston, MAGoogle Scholar
  8. 8.
    Douglass BP (2002) Real-time design patterns: robust scalable architecture for real-time systems. Addison-Wesley, Boston, MAGoogle Scholar
  9. 9.
    Duran-Limon HA, Castillo-Barrera FE, Lopez-Herrejon RE (2011) Towards an ontology-based approach for deriving product architectures. Proceeding of SPLC 2011 conference, vol. 2, Article 19, Munich, Germany, p 5Google Scholar
  10. 10.
    Feiler PH, Gluch DP, Hudak J (2006) The architecture analysis & design language (AADL): an introduction. Tech. Report CMU/SEI-2006-TN-011. SEI, CMU, USAGoogle Scholar
  11. 11.
    Gómez A, Ramos I (2010) Cardinality-based feature modeling and model-driven engineering: fitting them together. Proceeding of the VAMOS 2010 workshop, Linz, AustriaGoogle Scholar
  12. 12.
    Hudak J, Feiler P (2007) Developing AADL models for control systems: a practitioner’s guide. Tech. Report CMU/SEI-2007-TR-014, SEI, CMU, USAGoogle Scholar
  13. 13.
    ISO/IEC 25010:2011 (2011) Systems and software engineering, systems and software quality requirements and evaluation (SQuaRE), system and software quality modelsGoogle Scholar
  14. 14.
    Needham D, Jones S (2006) A software fault tree metric. In: Proceeding of 22nd IEEE international conference on software maintenance, Philadelphia, PA, USA, pp 401–410Google Scholar
  15. 15.
    OMG (2008) Meta Object Facility 2.0 Query/view/transformation specification.
  16. 16.
    Perovich D, Rossel PO, Bastarrica MC (2010) Feature model to product architectures: applying MDE to software product lines. Proceeding of WICSA/ECSA 2010 conference, Helsinki, Finland, pp 201–210Google Scholar
  17. 17.
    Saaty TL (1980) The analytical hierarchical process. McGraw-Hill, New York, NYGoogle Scholar
  18. 18.
    Shiraishi S (2010) An AADL-based approach to variability modeling of automotive control systems. Proceeding of the MODELS conference Oslo, Norway, LNCS 6394:346–360Google Scholar
  19. 19.
    Thiel S, Hein A (2002) Modeling and using product line variability in automotive systems. IEEE Software 19(4):66–72CrossRefGoogle Scholar
  20. 20.
    Zschaler S, Sanchez P, Santos J, Alferez M, Rashid A, Fuentes L, Moreira A, Araujo J, Kulesza U (2009) VML* a family of languages for variability management in software product lines. In Proceeding SLE 2009 conference, Denver, USAGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Emilio Insfran
    • 1
    Email author
  • Silvia Abrahão
    • 1
  • Javier González-Huerta
    • 1
  • John D. McGregor
    • 2
  • Isidro Ramos
    • 1
  1. 1.ISSI Research GroupUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Department of Computer ScienceClemson UniversityClemsonUSA

Personalised recommendations