Abstract
Instead of performing exhaustive testing that tests all possible combinations of input parameter values of a system, it is better to switch to a more efficient and effective testing technique i.e., pair wise testing. In pair wise testing, test cases are designed to cover all possible combinations of each pair of input parameter values. It has been shown that the problem of finding the minimum set of test cases for pair-wise testing is an NP complete problem. In this paper we apply genetic algorithm, a meta heuristic search algorithm, to find an optimal solution to the pair-wise test set generation problem. We present a method to generate initial population using hamming distance and an algorithm to find crossover points for combining individuals selected for reproduction. We describe the implementation of the proposed approach by extending an open source tool PWiseGen and evaluate the effectiveness of the proposed approach. Empirical results indicate that our approach can generate test sets with higher fitness level by covering more pairs of input parameter values.
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References
Cohen, D.M., Dalal, S.R., Kajla, A., Patton, G.C.: The automatic efficient test generator. In: Proceedings of the IEEE International Symposium on Software Reliability Engineering, pp. 303–309 (1994)
Cohen, D.M., Dalal, S.R., Fredman, M.L., Patton, G.C.: The combinatorial design approach to automatic test generation. IEEE Software, 83–87 (1996)
Cohen, D.M., Dalal, S.R., Fredman, M.L., Patton, G.C.: The AETG system: An approach to testing based on combinatorial design. IEEE Transactions on Software Engineering 23(7), 437–443 (1997)
Lei, Y., Tai, K.C.: In-parameter-order: A test generation strategy for pairwise testing. In: The 3rd IEEE International Symposium on High-Assurance Systems Engineering, HASE 1998, Washington, DC, pp. 254–261 (1998)
Yuan, X., Cohen, M.B., Memon, A.: Covering Array Sampling of Input Event Sequences for Automated GUI Testing. In: Proceedings of the 22nd International Conference on Automated Software Engineering, pp. 405–408 (2007)
Wang, W., Sampath, S., Lei, Y., Kacker, R.: An interaction – based test sequence generation approach for testing web applications. In: Proceedings of 11th Int’l IEEE HASE Symposium, pp. 209–218 (2008)
Nguyen, C.D., Marchetto, A., Tonella, P.: Combining model-based and combinatorial testing for effective test case generation. In: Proceedings of International Symposium on Software Testing and Analysis, ISSTA, pp. 100–110 (2012)
Mandl, R.: Orthogonal Latin Squares: An Application of Experiment Design to Compiler Testing. Communications of the ACM 28(10), 1054–1058 (1985)
Hartman, A.: Software and Hardware Testing Using Combinatorial Covering Suites. In: Graph Theory, Combinatorics and Algorithms. Operations Research/Computer Science Interfaces Series, vol. 34, pp. 237–266. Springer, US (2005)
Avila-George, H., Torres-Jimenez, J., Hernández, V., Gonzalez-Hernandez, L.: Simulated annealing for constructing mixed covering arrays. In: Omatu, S., Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., RodrÃguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 657–664. Springer, Heidelberg (2012)
Gonzalez-Hernandez, L., Rangel-Valdez, N., Torres-Jimenez, J.: Construction of Mixed Covering Arrays of Variable Strength Using a Tabu Search Approach. In: Wu, W., Daescu, O. (eds.) COCOA 2010, Part I. LNCS, vol. 6508, pp. 51–64. Springer, Heidelberg (2010)
Shiba, T., Tsuchiya, T., Kikuno, T.: Using artificial life techniques to generate test cases for combinatorial testing. In: Proceedings of the 28th Annual International Computer Software and Applications Conference, pp. 72–77. IEEE Computer Society (2004)
Jia-Ze, S., Shu-Yan, W.: Generation of Pairwise Test Sets using Novel DPSO algorithm. In: Yang, Y., Ma, M. (eds.) Green Communications and Networks. LNEE, vol. 113, pp. 479–487. Springer, Heidelberg (2012)
Ghazi, S.A., Ahmed, M.A.: Pair-wise test coverage using genetic algorithms. In: The 2003 Congress on Evolutionary Computation, vol. 2, pp. 1420–1423. IEEE Computer Society, Australia (2003)
McCaffrey, J.D.: Generation of pairwise test sets using a genetic algorithm. In: Proceedings of 33rd Annual IEEE International Computer Software and Applications Conference, pp. 626–631. IEEE Press, Los Alamitos (2009)
McCaffrey, J.D.: An empirical study of pairwise test set generation using a genetic algorithm. In: ITNG 2010: 6th International Conference on Information Technology: New Generations, pp. 992–997. IEEE Computer Society, Las Vegas (2010)
Flores, P., Cheon, Y.: Pwisegen: Generating test cases for pairwise testing using genetic algorithms. In: IEEE International Conference on Computer Science and Automation Engineering (CSAE), vol. 2, pp. 747–752 (2011)
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Bansal, P., Sabharwal, S., Malik, S., Arora, V., Kumar, V. (2013). An Approach to Test Set Generation for Pair-Wise Testing Using Genetic Algorithms. In: Ruhe, G., Zhang, Y. (eds) Search Based Software Engineering. SSBSE 2013. Lecture Notes in Computer Science, vol 8084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39742-4_27
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DOI: https://doi.org/10.1007/978-3-642-39742-4_27
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