Requirements-Driven Social Adaptation: Expert Survey

  • Malik Almaliki
  • Funmilade Faniyi
  • Rami Bahsoon
  • Keith Phalp
  • Raian Ali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8396)


[Context and motivation] Self-adaptation empowers systems with the capability to meet stakeholders’ requirements in a dynamic environment. Such systems autonomously monitor changes and events which drive adaptation decisions at runtime. Social Adaptation is a recent kind of requirements-driven adaptation which enables users to give a runtime feedback on the success and quality of a system’s configurations in reaching their requirements. The system analyses users’ feedback, infers their collective judgement and then uses it to shape its adaptation decisions. [Question/problem] However, there is still a lack of engineering mechanisms to guarantee a correct conduction of Social Adaptation. [Principal ideas/results] In this paper, we conduct a two-phase Expert Survey to identify core benefits, domain areas and challenges for Social Adaptation. [Contribution] Our findings provide practitioners and researchers in adaptive systems engineering with insights on this emerging role of users, or the crowd, and stimulate future research to solve the open problems in this area.


Requirements Engineering Adaptive Systems Social Adaptation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cheng, B.H.C., et al.: Software engineering for self-adaptive systems: A research roadmap. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 1–26. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Oreizy, P., Gorlick, M.M., Taylor, R.N., Heimhigner, D., Johnson, G., Medvidovic, N., Quilici, A., Rosenblum, D.S., Wolf, A.L.: An architecture-based approach to self-adaptive software. IEEE Intelligent Systems and Their Applications 14(3), 54–62 (1999)CrossRefGoogle Scholar
  3. 3.
    Fickas, S., Feather, M.S.: Requirements monitoring in dynamic environments. In: Proceedings of the Second IEEE International Symposium on Requirements Engineering, pp. 140–147. IEEE (1995)Google Scholar
  4. 4.
    Cheng, S.W., Huang, A.C., Garlan, D., Schmerl, B., Steenkiste, P.: Rainbow: Architecture-based self-adaptation with reusable infrastructure. In: International Conference on Autonomic Computing, pp. 276–277. IEEE (2004)Google Scholar
  5. 5.
    Ali, R., Solis, C., Omoronyia, I., Salehie, M., Nuseibeh, B.: Social adaptation at runtime. In: Maciaszek, L.A., Filipe, J. (eds.) ENASE 2012. CCIS, vol. 410, pp. 110–127. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Esfahani, N., Malek, S.: Social computing networks: a new paradigm for engineering self-adaptive pervasive software systems. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering, vol. 2, pp. 159–162. ACM (2010)Google Scholar
  7. 7.
    Pagano, D., Brügge, B.: User involvement in software evolution practice: A case study. In: Proceedings of the 2013 International Conference on Software Engineering, ICSE 2013, pp. 953–962. IEEE Press, Piscataway (2013)Google Scholar
  8. 8.
    Ali, R., Solis, C., Salehie, M., Omoronyia, I., Nuseibeh, B., Maalej, W.: Social sensing: When users become monitors. In: Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, ESEC/FSE 2011, pp. 476–479. ACM, New York (2011)Google Scholar
  9. 9.
    Sawyer, P., Bencomo, N., Whittle, J., Letier, E., Finkelstein, A.: Requirements-aware systems: A research agenda for re for self-adaptive systems. In: The 18th IEEE International Requirements Engineering Conference (RE), pp. 95–103. IEEE (2010)Google Scholar
  10. 10.
    Van Riemsdijk, B.: Socially adaptive software. Awareness Magazine (2013)Google Scholar
  11. 11.
    Pagano, D., Maalej, W.: User feedback in the appstore: An empirical study. In: The 21st IEEE International Requirements Engineering Conference (RE), pp. 125–134 (2013)Google Scholar
  12. 12.
    Linstone, H.A., Turoff, M.: The delphi method. Addison-Wesley Reading, MA (1975)MATHGoogle Scholar
  13. 13.
    Cooke, R.M., Probst, K.N.: Highlights of the Expert Judgment Policy Symposium and Technical Workshop. Resources for the Future, Washington, DC (2006)Google Scholar
  14. 14.
    Leung, W.C.: How to design a questionnaire. Student BMJ 9(11), 187–189 (2001)Google Scholar
  15. 15.
    Franklin, S., Walker, C.: Survey methods and practices. Statistics Canada, Social Survey Methods Division (2003)Google Scholar
  16. 16.
    Elkhodary, A., Esfahani, N., Malek, S.: Fusion: a framework for engineering self-tuning self-adaptive software systems. In: Proceedings of the Eighteenth ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 7–16. ACM (2010)Google Scholar
  17. 17.
    Tesauro, G., Jong, N.K., Das, R., Bennani, M.N.: A hybrid reinforcement learning approach to autonomic resource allocation. In: The IEEE International Conference on Autonomic Computing, ICAC 2006, pp. 65–73. IEEE (2006)Google Scholar
  18. 18.
    Likert, R.: A technique for the measurement of attitudes. Archives of Psychology (1932)Google Scholar
  19. 19.
    Adolphs, R.: Recognizing emotion from facial expressions: Psychological and neurological mechanisms. Behavioral and Cognitive Neuroscience Reviews 1(1), 21–62 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Malik Almaliki
    • 1
  • Funmilade Faniyi
    • 2
  • Rami Bahsoon
    • 2
  • Keith Phalp
    • 1
  • Raian Ali
    • 1
  1. 1.Bournemouth UniversityUK
  2. 2.University of BirminghamUK

Personalised recommendations