Advertisement

Applications of Model Reuse When Using Estimation of Distribution Algorithms to Test Concurrent Software

  • Jan Staunton
  • John A. Clark
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6956)

Abstract

Previous work has shown the efficacy of using Estimation of Distribution Algorithms (EDAs) to detect faults in concurrent software/systems. A promising feature of EDAs is the ability to analyse the information or model learned from any particular execution. The analysis performed can yield insights into the target problem allowing practitioners to adjust parameters of the algorithm or indeed the algorithm itself. This can lead to a saving in the effort required to perform future executions, which is particularly important when targeting expensive fitness functions such as searching concurrent software state spaces. In this work, we describe practical scenarios related to detecting concurrent faults in which reusing information discovered in EDA runs can save effort in future runs, and prove the potential of such reuse using an example scenario. The example scenario consists of examining problem families, and we provide empirical evidence showing real effort saving properties for three such families.

Keywords

Model Check Transition System Linear Temporal Logic Problem Family Distribution Algorithm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alba, E., Chicano, F.: Finding safety errors with ACO. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1066–1073. ACM Press, New York (2007)CrossRefGoogle Scholar
  2. 2.
    Alba, E., Chicano, F.: Searching for liveness property violations in concurrent systems with ACO. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1727–1734. ACM, New York (2008)CrossRefGoogle Scholar
  3. 3.
    Alba, E., Chicano, F., Ferreira, M., Gomez-Pulido, J.: Finding deadlocks in large concurrent java programs using genetic algorithms. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1735–1742. ACM, New York (2008)CrossRefGoogle Scholar
  4. 4.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. The MIT Press, Cambridge (2000)Google Scholar
  5. 5.
    Edelkamp, S., Lafuente, A.L., Leue, S.: Directed explicit model checking with HSF-SPIN. In: Proceedings of the 8th International SPIN Workshop on Model Checking of Software, pp. 57–79. Springer-Verlag New York, Inc., New York (2001)Google Scholar
  6. 6.
    Edelkamp, S., Leue, S., Lluch-Lafuente, A.: Protocol verification with heuristic search. In: AAAI-Spring Symposium on Model-based Validation Intelligence, pp. 75–83 (2001)Google Scholar
  7. 7.
    Luke, S., Panait, L., Balan, G., et al.: Ecj 16: A java-based evolutionary computation research system (2007)Google Scholar
  8. 8.
    Pelikan, M., Goldberg, D.E., Lobo, F.G.: A survey of optimization by building and using probabilistic models. Computational Optimization and Applications 21(1), 5–20 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Poli, R., McPhee, N.F.: A linear estimation-of-distribution GP system. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 206–217. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Russell, S.J., Norvig, P., Canny, J.F., Malik, J., Edwards, D.D.: Artificial intelligence: a modern approach. Prentice hall, Englewood Cliffs (1995)Google Scholar
  11. 11.
    Staunton, J., Clark, J.A.: Searching for safety violations using estimation of distribution algorithms. In: IEEE International Conference on Software Testing, Verification, and Validation Workshop, pp. 212–221 (2010)Google Scholar
  12. 12.
    Staunton, S., Clark, J.A.: Finding short counterexamples in promela models using estimation of distribution algorithms. To appear: Search-based Software Engineering Track, Genetic and Evolutionary Computation Conference (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jan Staunton
    • 1
  • John A. Clark
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkUK

Personalised recommendations