Evolutionary Multi-Objective Optimization for Data-Flow Testing of Object-Oriented Programs

  • P. Maragathavalli
  • S. Kanmani
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


This paper presents a Class-Based Elitist Genetic Algorithm (CBEGA) to generate a suite of tests for testing the object-oriented programs using evolutionary multi-objective optimization techniques. Evolutionary Algorithms (EAs) are inspired by mechanisms in biological evolution like reproduction, mutation, recombination, and selection. EA applies these mechanisms repeatedly to a set of individuals called population to obtain solution. Multi-objective optimization involves optimizing a number of objectives simultaneously. The objectives considered in this paper for optimization are maximum coverage, minimum execution time and test-suite minimization. The experiment shows that CBEGA gives 92% path coverage and simple GA gives 88% path coverage for a set of java classes.


Evolutionary Algorithm multi-objective path coverage CBEGA immigration rate 


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  1. 1.
    Sukstrienwong, A.: Solving multi-objective optimization under bounds by genetic algorithms. International Journal of Computers 5(1), 18–25 (2011)Google Scholar
  2. 2.
    Ghiduk, A.S.: Automatic Generation of Object-Oriented Tests with a Multistage-Based Genetic Algorithm. Journal of Computers 5(10), 1560–1569 (2010)CrossRefGoogle Scholar
  3. 3.
    Singh, D.P., Khare, A.: Different Aspects of Evolutionary Algorithms, Multi-Objective Optimization Algorithms and Application Domain. International Journal of Advanced Networking and Applications 2(04), 770–775 (2011)Google Scholar
  4. 4.
    Harman, M., Kiranlakhotia, McMinn, P.: A Multi-Objective Approach to Search-Based Test Data Generation. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007, pp. 1–8 (2007)Google Scholar
  5. 5.
    Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety, pp. 992–1007. Elsevier (2006)Google Scholar
  6. 6.
    Conway, B.A.: A Survey of Methods Available for the Numerical Optimization of Continuous Dynamic Systems. Journal of Optimization Theory and Applications (JOTA) of Springer, 1–36 (2011)Google Scholar
  7. 7.
    Malhotra, R., Garg, M.: An Adequacy Based Test Data Generation Technique Using Genetic Algorithms. Journal of Information Processing Systems 7(2), 363–384 (2011)CrossRefGoogle Scholar
  8. 8.
    Andreou, A.S., Economides, K.A., Sofokleous, A.A.: An Automatic software test-data generation scheme based on data flow criteria and genetic algorithms. In: 7th International Conference on Computer and Information Technology, pp. 867–872 (2007)Google Scholar
  9. 9.
    Chen, Y., Zhong, Y.: Automatic Path-oriented Test Data Generation Using a Multi-population Genetic Algorithm. In: Fourth International Conference on Natural Computation, pp. 566–570 (2008)Google Scholar
  10. 10.
    Deb, K.: Single and Multi-Objective Optimization using Evolutionary Computation. KanGALRt- No. 2004002, Technical Report, pp. 1–24 (2005)Google Scholar
  11. 11.
    Zhang, Y.: Multi-Objective Search-Based Requirements Selection and Optimization. Ph.D Thesis, University of London, pp. 1–276 (2010)Google Scholar
  12. 12.
    Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer (2007)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Information TechnologyPondicherry Engineering CollegePuducherryIndia

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