Using Design Metrics for Predicting System Flexibility

  • Robby
  • Scott A. DeLoach
  • Valeriy A. Kolesnikov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3922)


While multiagent systems have been extolled as dynamically configurable and capable of emergent behavior, these qualities can be a drawback. When the system changes so that it no longer achieves its goals, emergent behavior is undesirable. Giving agents the autonomy to adapt and then expecting them to adapt only in acceptable ways requires rigorous design analyses. In this paper, we propose metrics for determining system flexibility at design time. Our approach is based on organization-based multiagent systems, which allows multiagent systems to adapt within a preset structure. We tailored the Bogor model checker to efficiently analyze the adaptive behaviors of these systems and to determine their properties such as fault-tolerance and cost-efficiency. We develop state-space coverage metrics to allow designers to make informed trade-offs at design-time between computational cost and system flexibility.


Model Check Multiagent System Goal Achievement Goal Model Emergent Behavior 


  1. 1.
  2. 2.
    Albus, J.S.: Metrics and Performance Measures for Intelligent Unmanned Ground Vehicles. In: Proceedings of the 2002 Performance Metrics for Intelligent Systems Workshop (2002)Google Scholar
  3. 3.
    Blau, P.M., Scott, W.R.: Formal Organizations, Chandler, San Fran., CA, pp. 194–221 (1962)Google Scholar
  4. 4.
  5. 5.
    Bosnacki, D., Dams, D., Holenderski, L.: Symmetric Spin. In: Havelund, K., Penix, J., Visser, W. (eds.) SPIN 2000. LNCS, vol. 1885, pp. 1–19. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Clarke, E., Grumberg, O., Peled, D.: Model Checking. MIT Press, Cambridge (2000)Google Scholar
  7. 7.
    DeLoach, S.A., Matson, E.: An Organizational Model for Designing Adaptive Multiagent Systems. In: The AAAI-04 Workshop on Agent Organizations: Theory and Practice (2004)Google Scholar
  8. 8.
    Dwyer, M.B., Hatcliff, J., Robby, P.V.R.: Exploiting Object Escape and Locking Information in Partial Order Reduction for Concurrent Object-Oriented Programs. International Journal of Formal Methods in System Design (FMSD) 25(2/3), 199–240 (2004)CrossRefMATHGoogle Scholar
  9. 9.
    Ericson, C.: Fault Tree Analysis – A History. In: Proceedings of the 17th International System Safety Conference – (1999)Google Scholar
  10. 10.
    Fenton, N.E., Neil, M.: Software metrics: roadmap. ICSE-Future of SE, 357–370 (2000)Google Scholar
  11. 11.
    Holzmann, G.J.: State Compression in SPIN: Recursive Indexing and Compression Training Runs. In: Proceedings of the Third International SPIN Workshop (1997)Google Scholar
  12. 12.
    Ip, C.N., Dill, D.L.: Better Verification Through Symmetry. International Journal of Formal Methods in System Design (FMSD) 9(1/2), 47–75 (1996)Google Scholar
  13. 13.
    Liu, Y.A., Stoller, S.D.: Querying Complex Graphs. In: Proceedings of the Eighth Intl Symposium on Practical Aspects of Declarative Languages (PADL), Springer, Heidelberg (to appear, 2006)Google Scholar
  14. 14.
    Matson, E., DeLoach, S.: Capability in Organization Based Multi-agent Systems. In: Proceedings of the Intelligent and Computer Systems (IS 2003) Conference (2003)Google Scholar
  15. 15.
    Robby, D.M.B., Hatcliff, J.B.: An Extensible and Highly-Modular Model Checking Framework. In: Proceedings of the 9th European Software Engineering Conference held jointly with the 11th ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2003), pp. 267–276 (2003)Google Scholar
  16. 16.
    Robby, D.M.B., Hatcliff, J., Iosif, R.: Space-Reduction Strategies for Model Checking Dynamic Software. In: Proceedings of the 2nd Workshop on Software Model Checking (SoftMC 2003). Electronic Notes in Theoretical Computer Science, vol. 89 (3), Elsevier, Amsterdam (2003)Google Scholar
  17. 17.
    Subramanian, N., Chung, L.: Metrics for Software Adaptability, Applied Technology Division, Anritsu Company, Richardson, TX, USA (2000)Google Scholar
  18. 18.
    van Lamsweerde, A., Darimont, R., Letier, E.: Managing conflicts in goal-driven requirements engineering. IEEE Transactions on Software Engineering 24(11), 908–926 (1998)CrossRefGoogle Scholar
  19. 19.
    Verkamo, A.I., Gustafsson, J., Nenonen, L., Paakki, J.: Design patterns in performance prediction. In: Proceedings of the Second International Workshop on Software and Performance, September 2000, pp. 143–144. ACM Press, New York (2000)CrossRefGoogle Scholar
  20. 20.
    Weyuker, E.J., Avritzer, A.: A metric for predicting the performance of an application under a growing workload. IBM Systems Journal 41(1), 45–54 (2002)CrossRefGoogle Scholar
  21. 21.
    Weyuker, E.J., Avritzer, A.: A Metric to Predict Software Scalability. In: Proceedings of the Eight IEEE Symp. on Software Metrics (METRICS 2002), Ottawa, Canada, pp. 152–159 (June 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Robby
    • 1
  • Scott A. DeLoach
    • 1
  • Valeriy A. Kolesnikov
    • 1
  1. 1.Department of Computing and Information SciencesKansas State UniversityManhattanUSA

Personalised recommendations