Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks

  • Nelly Bencomo
  • Amel Belaggoun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7830)

Abstract

[Context/Motivation] Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. [Questions/Problems] One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. [Principal ideas/results] In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs.

Keywords

requirements specification-methodologies goal models dynamic decision networks bayesian decision theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Norsys software corporation. netica - user guide (1997)Google Scholar
  2. 2.
    Belaggoun, A.: Exploring the Use of Dynamic Decision Networks for Self-Adaptive Systems. Master’s thesis, Univ. de Versailles Saint-Quentin-En-Yvelines (2012)Google Scholar
  3. 3.
    Cheng, B.H., de Lemos, R., Giese, H., Inverardi, P., Magee, J.: 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
  4. 4.
    Chung, L., Nixon, B.A., Yu, E., Mylopoulos, J.: Non-Functional Requirements in Software Engineering, vol. 5. Springer (1999)Google Scholar
  5. 5.
    da Costa, P.C.G.: The Fighter Aircrafts Autodefense Management Problem: A Dynamic Decision Network Approach. Master’s thesis, School of Information Technology and Engineering, George Mason University (1999)Google Scholar
  6. 6.
    Fenton, N.E., Neil, M.: Making decisions: using bayesian nets and mcda. Knowl.-Based Syst. 14(7), 307–325 (2001)CrossRefGoogle Scholar
  7. 7.
    Giorgini, P., Mylopoulos, J., Nicchiarelli, E., Sebastiani, R.: Formal reasoning techniques for goal models. In: Spaccapietra, S., March, S., Aberer, K. (eds.) Journal on Data Semantics. LNCS, vol. 2800, pp. 1–20. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Goldsby, H.J., Sawyer, P., Bencomo, N., Hughes, D., Cheng, B.H.: Goal-based modeling of dynamically adaptive system requirements. In: IEEE Int. Conference on the Engineering of Computer Based Systems, ECBS (2008)Google Scholar
  9. 9.
    Horvitz, E.J., Breese, J.S., Henrion, M.: Decision theory in expert systems and artificial intelligence. Int. Journal of Approximate Reasoning 2, 247–302 (1988)CrossRefGoogle Scholar
  10. 10.
    Howard, R., Matheson., J.: Influence diagrams. In: Readings on the Principles and Readings on the Principles and Applications of Decision Analysis II. Strategic Decisions Group, Menlo Park (1984)Google Scholar
  11. 11.
    Hughes, D., Greenwood, P., Coulson, G., Blair, G.: Gridstix: Supporting flood prediction using embedded hardware and next generation grid middleware. In: Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks, pp. 621–626. IEEE Computer Society, USA (2006)CrossRefGoogle Scholar
  12. 12.
    Lapouchnian, A.: Exploiting Requirements Variability for Software Customization and Adaptation. Ph.D. thesis, University of Toronto (2011)Google Scholar
  13. 13.
    de Lemos, R., Giese, H., Müller, H., Shaw, M.: Software Engineering for Self-Adpaptive Systems: A second Research Roadmap. In: Software Engineering for Self-Adaptive Systems. No. 10431 in Dagstuhl Seminar Proceedings, Schloss Dagstuhl, Germany (2011)Google Scholar
  14. 14.
    Letier, E., van Lamsweerde, A.: Reasoning about partial goal satisfaction for requirements and design engineering. SIGSOFT Softw. Eng. Notes 26 (2004)Google Scholar
  15. 15.
    Liaskos, S., McIlraith, S.A., Sohrabi, S., Mylopoulos, J.: Representing and reasoning about preferences in requirements engineering. Requir. Eng. 16(3), 227–249 (2011)CrossRefGoogle Scholar
  16. 16.
    Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)Google Scholar
  17. 17.
    Portinale, L., Raiteri, D.C.: Using dynamic decision networks and extended fault trees for autonomous fdir. In: ICTAI, pp. 480–484 (2011)Google Scholar
  18. 18.
    Qureshi, N.A., Peini, A.: Engineering adaptive requirements. In: Workshop on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2009 (2009)Google Scholar
  19. 19.
    Ramirez, A.J., Cheng, B.H.C., Bencomo, N., Sawyer, P.: Relaxing claims: Coping with uncertainty while evaluating assumptions at run time. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 53–69. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Russell, S.J., Norvig, P.: Artificial intelligence - a modern approach: the intelligent agent book. Prentice Hall series in artificial intelligence. Prentice Hall (1995)Google Scholar
  21. 21.
    Russell, S.J., Norvig, P.: Artificial intelligence: A modern approach, 2nd edn. Prentice Hall series in artificial intelligence. Prentice Hall (2003)Google Scholar
  22. 22.
    Sawyer, P., Bencomo, N., Letier, E., Finkelstein, A.: Requirements-aware systems: A research agenda for re self-adaptive systems. In: Proc. of the 18th IEEE International Requirements Engineering Conference, pp. 95–103 (2010)Google Scholar
  23. 23.
    Welsh, K., Sawyer, P., Bencomo, N.: Towards requirements aware systems: Run-time resolution of design-time assumptions. In: ASE, pp. 560–563 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nelly Bencomo
    • 1
  • Amel Belaggoun
    • 1
  1. 1.INRIA Paris - RocquencourtFrance

Personalised recommendations