Abstract
Search based technologies have been widely used in regression test suite optimization, including test case prioritization, test case selection and test suite minimization, to improve the efficiency and reduce the cost of testing. Unlike test case selection and test suite minimization, the evaluation of test case prioritization is based on the test case execution sequence, in which genetic algorithm is one of the most popular algorithms employed. When permutation encoding is used to represent the execution sequence, the execution of previous test cases can affect the presence of the following test cases, namely epistatic effect. In this paper, the application of epistatic domains theory in genetic algorithms for test case prioritization is analyzed, where Epistatic Test Case Segment is defined. Two associated crossover operators are proposed based on epistasis. The empirical studies show that the proposed two-point crossover operator, E-Ord, outperform the crossover PMX, and can produce higher fitness with a faster convergence.
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Yoo, S., Harman, M.: Regression testing minimization, selection and prioritization: a survey. Softw. Test. Verif. Reliab. 22(2), 67–120 (2012)
Wong, W.E., Horgan, J.R., London, S., Mathur, A.P.: Effect of test set minimization on fault detection effectiveness. In: 17th International Conference on Software Engineering, ICSE 1995, p. 41. IEEE (1995)
Li, Z., Harman, M., Hierons, R.M.: Search algorithms for regression test case prioritization. IEEE Trans. Softw. Eng. 33(4), 225–237 (2007)
Li, Z., Bian, Y., Zhao, R., Cheng, J.: A fine-grained parallel multi-objective test case prioritization on GPU. In: Ruhe, G., Zhang, Y. (eds.) SSBSE 2013. LNCS, vol. 8084, pp. 111–125. Springer, Heidelberg (2013)
Singh, Y., Kaur, A., Suri, B.: Test case prioritization using ant colony optimization. ACM SIGSOFT Software Eng. Notes 35(4), 1–7 (2010)
Hla, K.H.S., Choi, Y., Park, J.S.: Applying particle swarm optimization to prioritizing test cases for embedded real time software. In: Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops, Sydney, Australia, pp. 527–532. IEEE, 8–11 July 2008
Davis, L.: Applying adaptive algorithms to epistatic domains. In: IJCAI, vol. 85, pp. 162–164 (1985)
Harman, M., Jones, B.F.: Search-based software engineering. Inf. Softw. Technol. 43(14), 833–839 (2001)
Rothermel, G., Untch, R.H., Chu, C., Harrold, M.J.: Prioritizing test cases for regression testing. IEEE Trans. Softw. Eng. 27(10), 929–948 (2001)
Smith, J.M., et al.: Evolutionary Genetics. Oxford University Press, Oxford (1989)
Beaslev, D., Bull, D.R., Martin, R.R.: An overview of genetic algorithms: Part 2, research topics. Univ. Comput. 15(4), 170–181 (1993)
Davidor, Y.: Epistasis variance: suitability of a representation to genetic algorithms. Complex Syst. 4(4), 369–383 (1990)
Paixão, T., Barton, N.: A variance decomposition approach to the analysis of genetic algorithms. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, pp. 845–852. ACM (2013)
Rochet, S., Slimane, M., Venturini, G.: Epistasis for real encoding in genetic algorithms. In: Australian and New Zealand Conference on Intelligent Information Systems, pp. 268–271. IEEE (1996)
Seo, D.I., Moon, B.R.: Voronoi quantizied crossover for traveling salesman problem. In: GECCO, pp. 544–552 (2002)
Larrañaga, P., Kuijpers, C.M.H., Murga, R.H., Inza, I., Dizdarevic, S.: Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif. Intell. Rev. 13(2), 129–170 (1999)
Beasley, D., Martin, R., Bull, D.: An overview of genetic algorithms: Part 1. fundamentals. Univ. Comput. 15, 58 (1993)
Arcuri, A., Briand, L.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: 2011 33rd International Conference on Software Engineering (ICSE), pp. 1–10. IEEE (2011)
Singh, Y., Kaur, A., Suri, B.: Test case prioritization using ant colony optimization. ACM SIGSOFT Softw. Eng. Notes 35(4), 1–7 (2010)
Epitropakis, M.G., Yoo, S., Harman, M., Burke, E.K.: Pareto efficient multi-objective regression test suite prioritisation. Techreport 14(01), 01 (2014)
Yoo, S., Harman, M., Ur, S.: Highly scalable multi objective test suite minimisation using graphics cards. In: Cohen, M.B., Ó Cinnéide, M. (eds.) SSBSE 2011. LNCS, vol. 6956, pp. 219–236. Springer, Heidelberg (2011)
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybern. 24(4), 656–667 (1994)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)
Acknowledgments
The work described in this paper is supported by the National Natural Science Foundation of China under Grant No. 61170082 and 61472025, the Program for New Century Excellent Talents in University (NCET-12-0757) and SRF for ROCS, SEM (LXJJ201303).
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Yuan, F., Bian, Y., Li, Z., Zhao, R. (2015). Epistatic Genetic Algorithm for Test Case Prioritization. In: Barros, M., Labiche, Y. (eds) Search-Based Software Engineering. SSBSE 2015. Lecture Notes in Computer Science(), vol 9275. Springer, Cham. https://doi.org/10.1007/978-3-319-22183-0_8
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DOI: https://doi.org/10.1007/978-3-319-22183-0_8
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