Generating Test Cases for Constraint Automata by Genetic Symbiosis Algorithm

  • Samira Tasharofi
  • Sepand Ansari
  • Marjan Sirjani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4260)


Constraint automata are a semantic model for Reo modeling language. Testing correctness of mapping black-box components in Reo to constraint automata is an important problem in analyzing the semantic model of Reo. This testing requires a suite of test cases that cover the automaton states and transitions and also examine different paths. In this paper, Genetic Algorithm (GA) is employed to generate such suite of test cases. This test data generation is improved by Genetic Symbiosis Algorithm (GSA). The results show that GSA approach brings us a suite of test cases with full coverage of automata states and transitions and also diversity of examined paths.


Constraint automata finite-state machine testing automatic test data generation genetic algorithms symbiotic evolutionary algorithms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arbab, F.: Reo: A channel-based coordination model for component composition. Mathematical Structures in Computer Science 14(3), 329–366 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Baier, C., Sirjani, M., Arbab, F., Rutten, J.J.: Modeling component connectors in Reo by constraint automata. Science of Computer Programming (accepted, 2005) (to appear)Google Scholar
  3. 3.
    Pargas, R.P., Harrold, M.J., Peck, R.: Test-data generation using genetic algorithms. Software Testing, Verification & Reliability 9(4), 263–282 (1999)CrossRefGoogle Scholar
  4. 4.
    Wegener, J., Baresel, A., Sthamer, H.: Evolutionary test environment for automatic structural testing. Information & Software Technology 43(14), 841–854 (2001)CrossRefGoogle Scholar
  5. 5.
    Tracey, N., Clark, J., Mander, K.: Automated program flaw finding using simulated annealing. In: Proceedings of the 1998 ACM SIGSOFT international symposium on Software testing and analysis(ISSTA 1998), pp. 73–81. ACM Press, New York (1998)CrossRefGoogle Scholar
  6. 6.
    Watkins, A.: The automatic generation of software test data using genetic algorithms. In: Proceedings of the Fourth Software Quality Conference, Dundee, Scotland, vol. 2, pp. 300–309 (1995)Google Scholar
  7. 7.
    Borgelt, K.: Software Test Data Generation from a Genetic Algorithm. In: Industrial Applications of Genetic Algorithms. CRC Press, Boca Raton (1998)Google Scholar
  8. 8.
    Lin, J.C., Yeh, P.L.: Automatic test data generation for path testing using GAs. Inf. Sci. 131(1-4), 47–64 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Michael, C.C., McGraw, G., Schatz, M.A.: Generating software test data by evolution. IEEE Trans. Softw. Eng. 27(12), 1085–1110 (2001)CrossRefGoogle Scholar
  10. 10.
    Alander, J.T., Mantere, T., Turunen, P.: Genetic algorithm based software testing. In: Artificial Neural Nets and Genetic Algorithms, Wien, Austria, pp. 325–328. Springer, Heidelberg (1998)Google Scholar
  11. 11.
    Chow, T.S.: Testing software design modeled by finite-state machines. IEEE Trans. Software Eng. 4(3), 178–187 (1978)CrossRefGoogle Scholar
  12. 12.
    Lee, D., Yannakakis, M.: Principles and methods of testing finite state machines - A survey. Proceedings of the IEEE 84, 1090–1126 (1996)CrossRefGoogle Scholar
  13. 13.
    Belinfante, A., Feenstra, J., de Vries, R.G., Tretmans, J., Goga, N., Feijs, L.M.G., Mauw, S., Heerink, L.: Formal test automation: A simple experiment. In: 12th Int. Workshop on Testing of Communicating Systems (IWTCS), pp. 179–196. Kluwer, Dordrecht (1999)Google Scholar
  14. 14.
    Brinksma, E., Tretmans, J.: Testing transition systems: An annotated bibliography. In: Cassez, F., Jard, C., Rozoy, B., Dermot, M. (eds.) MOVEP 2000. LNCS, vol. 2067, pp. 187–195. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Clarke, D., Jeron, T., Rusu, V., Zinovieva, E.: STG: A symbolic test generation tool. In: Katoen, J.-P., Stevens, P. (eds.) TACAS 2002. LNCS, vol. 2280, pp. 470–475. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Fernandez, J.C., Jard, C., Jeron, T., Viho, G.: Using on-the-fly verification techniques for the generation of test suites. In: Alur, R., Henzinger, T.A. (eds.) CAV 1996. LNCS, vol. 1102, pp. 348–359. Springer, Heidelberg (1996)Google Scholar
  17. 17.
    Tretmans, J.: Testing techniques. Lecture notes, University of Twente, The Netherlands (2002)Google Scholar
  18. 18.
    Hennie, F.C.: Fault detecting experiments for sequential circuits. In: FOCS, pp. 95–110 (1964)Google Scholar
  19. 19.
    Sabnani, K., Dahbura, A.: A protocol test generation procedure. Comput. Netw. ISDN Syst. 15(4), 285–297 (1988)CrossRefGoogle Scholar
  20. 20.
    Naito, S., Tsunoyama, M.: Fault detection for sequential machines by transitions tours. In: Proceedings of IEEE Fault Tolerant Computing Symposium, pp. 238–243. IEEE Computer Society Press, Los Alamitos (1981)Google Scholar
  21. 21.
    Fujiwara, S., von Bochmann, G., Khendek, F., Amalou, M., Ghedamsi, A.: Test selection based on finite state models. IEEE Trans. Softw. Eng. 17(6), 591–603 (1991)CrossRefGoogle Scholar
  22. 22.
    Luo, G., von Bochmann, G., Petrenko, A.: Test selection based on communicating nondeterministic finite-state machines using a generalized Wp-Method. IEEE Trans. Softw. Eng. 20(2), 149–162 (1994)CrossRefGoogle Scholar
  23. 23.
    Krichen, M., Tripakis, S.: Black-box conformance testing for real-time systems. In: Graf, S., Mounier, L. (eds.) SPIN 2004. LNCS, vol. 2989, pp. 109–126. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  24. 24.
    Krichen, M., Tripakis, S.: Real-time testing with timed automata testers and coverage criteria. In: Lakhnech, Y., Yovine, S. (eds.) FORMATS 2004 and FTRTFT 2004. LNCS, vol. 3253, pp. 134–151. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  25. 25.
    Higashino, T., Nakata, A., Taniguchi, K., Cavalli, A.R.: Generating test cases for a timed I/O automaton model. In: Proceedings of the IFIP TC6 12th International Workshop on Testing Communicating Systems, pp. 197–214. Kluwer, Deventer (1999)Google Scholar
  26. 26.
    Arbab, F., Rutten, J.: A coinductive calculus of component connectors. In: Wirsing, M., Pattinson, D., Hennicker, R. (eds.) WADT 2003. LNCS, vol. 2755, pp. 34–55. Springer, Heidelberg (2003), CrossRefGoogle Scholar
  27. 27.
    Ghassemi, F., Tasharofi, S., Sirjani, M.: Automated mapping of reo circuits to constraint automata. Electr. Notes Theor. Comput. Sci. 159, 99–115 (2006)CrossRefGoogle Scholar
  28. 28.
    Ghadiri, A.: A tool for constraint automata join, BS project. Technical report, ECE Department University of Tehran (2004)Google Scholar
  29. 29.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  30. 30.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Boston (1989)Google Scholar
  31. 31.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)Google Scholar
  32. 32.
    Hirasawa, K., Ishikawa, Y., Hu, J., Murata, J., Mao, J.: Genetic symbiosis algorithm. In: Proc. of the 2000 Congress on Evolutionary Computation, pp. 1377–1384. IEEE Service Center, Piscataway (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Samira Tasharofi
    • 1
  • Sepand Ansari
    • 1
  • Marjan Sirjani
    • 1
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of TehranTehranIran
  2. 2.School of Computer ScienceInstitute for Studies in Theoretical Physics and Mathematics (IPM)TehranIran

Personalised recommendations