No Access

Generating Optimal Class Integration Test Orders Using Genetic Algorithms

Mine Digitalization Engineering Research Center of the Ministry of Education, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P. R. China

E-mail Address: ymzhang@cumt.edu.cn

Search for more papers by this author

Shujuan Jiang

Mine Digitalization Engineering Research Center of the Ministry of Education, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P. R. China

E-mail Address: shjjiang@cumt.edu.cn

Corresponding authors.

Search for more papers by this author

Yanru Ding

Mine Digitalization Engineering Research Center of the Ministry of Education, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P. R. China

Search for more papers by this author

Guan Yuan

Mine Digitalization Engineering Research Center of the Ministry of Education, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P. R. China

Search for more papers by this author

Junjie Liu

Mine Digitalization Engineering Research Center of the Ministry of Education, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P. R. China

Search for more papers by this author

Dongyu Lu

Mine Digitalization Engineering Research Center of the Ministry of Education, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P. R. China

Search for more papers by this author

, and

Junyan Qian

Guangxi Key Laboratory of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, Guangxi Zhuang Autonomous Region 541004, P. R. China

E-mail Address: qianjunyan@gxnu.edu.cn

Corresponding authors.

Search for more papers by this author

https://doi.org/10.1142/S0218194022500309Cited by:3 (Source: Crossref)

Abstract

In recent years, many intelligent optimization algorithms have been applied to the class integration and test order (CITO) problem. These algorithms also have been proved to be able to efficiently solve the problem. Here, the design of fitness function is a key task to generate the optimal solution. To better solve the class integration and test order problem, we propose a new fitness function to generate the optimal solution that achieves a balanced compromise between the different measures (objectives) such as the total number of stubs and the total stubbing complexity in this paper. We used some programs to compare and evaluate the different approaches. The experimental results show that our proposed approach is encouraging to some extent in solving the class integration and test order problem.

Keywords:

References

1. S. R. Vergilio, A. Pozo, J. C. Arias, R. da Veiga Cabral and T. Nobre, Multi-objective optimization algorithms applied to the class integration and test order problem, Int. J. Softw. Tools Technol. Transf. 14(4) (2012) 461–475. Crossref, Google Scholar
2. K. C. Tai and F. J. Daniels, Test order for inter-class integration testing of object-oriented software, in Proc. 21st Int. Computer Software and Applications Conf. (IEEE, 1997), pp. 602–607. Google Scholar
3. A. Abdurazik and J. Offutt, Coupling-based class integration and test order, in Proc. 2006 Int. Workshop Automation of Software Test (ACM, 2006), pp. 50–56. Crossref, Google Scholar
4. M. Zhang, S. J. Jiang, Y. M. Zhang, X. Y. Wang and Q. Yu, A multi-level feedback approach for the class integration and test order problem, J. Syst. Softw. 133 (2017) 54–67. Crossref, Web of Science, Google Scholar
5. L. C. Briand, J. Feng and Y. Labiche, Experimenting with genetic algorithms and coupling measures to devise optimal integration test orders, Technical Report No. SCE-02-03, Carleton University, 2002. Google Scholar
6. L. C. Briand, J. Feng and Y. Labiche, Using genetic algorithms and coupling measures to devise optimal integration test orders, in Proc. 14th Int. Conf. Software Engineering and Knowledge Engineering (ACM, 2002), pp. 43–50. Crossref, Google Scholar
7. D. Kung, J. Gao, P. Hsia, Y. Toyoshima and C. Chen, A test strategy for object-oriented programs, in Proc. 9th Annu. Int. Computer Software and Applications Conf. (IEEE, 1995), pp. 239–244. Crossref, Google Scholar
8. D. Y. He, J. Q. Xu and X. L. Chen, Information-theoretic-entropy based weight aggregation method in multiple-attribute group decision-making, Entropy 18(6) (2016) 171. Crossref, Web of Science, Google Scholar
9. Y. Le Traon, T. Jéron, J.-M. Jézéquel and P. Morel, Efficient object-oriented integration and regression test, IEEE Trans. Reliab. 49(1) (2000) 12–25. Crossref, Web of Science, Google Scholar
10. L. C. Briand, Y. Labiche and Y. Wang, An investigation of graph-based class integration test order strategies, IEEE Trans. Softw. Eng. 29(7) (2003) 594–607. Crossref, Web of Science, Google Scholar
11. A. Abdurazik and A. J. Offutt, Using coupling-based weights for the class integration and test order problem, Comput. J. 52(5) (2009) 557–570. Crossref, Web of Science, Google Scholar
12. Z. S. Wang and B. X. Li, Using coupling measure technique and random iterative algorithm for inter-class integration test order problem, in Proc. 34th Annu. IEEE Computer Software and Applications Conf. Workshops (IEEE, 2010), pp. 329–334. Crossref, Google Scholar
13. Y. M. Zhang, S. J. Jiang, G. Yuan, X. L. Ju and H. C. Zhang, An approach of class integration test order determination based on test levels, Softw.: Pract. Exp. 45 (2015) 657–687. Crossref, Web of Science, Google Scholar
14. R. da Veiga Cabral, A. Pozo and S. R. Vergilio, A pareto ant colony algorithm applied to the class integration and test order problem, in ICTSS 2010: Testing Software and Systems, Lecture Notes in Computer Science, Vol. 6435 (Springer, Heidelberg, 2010), pp. 16–29. Crossref, Google Scholar
15. D. Kalyanmoy, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II, in PPSN 2000: Parallel Problem Solving from Nature PPSN VI, Lecture Notes in Computer Science, Vol. 1917 (Springer, Heidelberg, 2000), pp. 849–858. Google Scholar
16. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6(2) (2002) 182–197. Crossref, Web of Science, Google Scholar
17. M. Gendreau and J. Y. Potvin, Tabu search, in Handbook of Metaheuristics, International Series in Operations Research and Management Science, Vol. 272 (Springer, Cham, 2010), pp. 41–59. Crossref, Google Scholar
18. H. Ishibuchi, Y. Sakane, N. Tsukamoto and Y. Nojima, Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations, in Proc. 2009 IEEE Int. Conf. Systems, Man, and Cybernetics (IEEE, 2009), pp. 1758–1763. Crossref, Google Scholar
19. W. K. G. Assunção, T. E. Colanzi, A. T. R. Pozo and S. R. Vergilio, Establishing integration test orders of classes with several coupling measures, in Proc. 13th Annu. Conf. Companion on Genetic and Evolutionary Computation (ACM, 2011), pp. 1867–1874. Crossref, Google Scholar
20. E. Zitzler, M. Laumanns and L. Thiele, SPEA2: Improving the strength pareto evolutionary algorithm, Technical Report No. TIK-Report 103, ETH Zurich, Switzerland, 2001. Google Scholar
21. W. K. G. Assunção, T. E. Colanzi, S. R. Vergilio and A. Pozo, A multi-objective optimization approach for the integration and test order problem, Inf. Sci. 267(20) (2014) 119–139. Crossref, Web of Science, Google Scholar
22. G. Guizzo, G. M. Fritsche, S. R. Vergilio, T. R. Aurora and A. Pozo, A hyper-heuristic for the multi-objective integration and test order problem, in Proc. 2015 Annu. Conf. Genetic and Evolutionary Computation (ACM, 2015), pp. 1343–1350. Crossref, Google Scholar
23. T. Mariani, G. Guizzo, S. R. Vergilio and A. Poza, Grammatical evolution for the multi-objective integration and test order problem, in Proc. Genetic and Evolutionary Computation Conf. (ACM, 2016), pp. 1069–1076. Crossref, Google Scholar
24. Y. M. Zhang, S. J. Jiang, X. Y. Wang, R. Y. Chen and M. Zhang, An optimization algorithm applied to the class integration and test order problem, Soft Comput. 23 (2019) 4239–4253. Crossref, Web of Science, Google Scholar
25. S. J. Jiang, M. Zhang, Y. M. Zhang, R. C. Wang, Q. Yu and J. W. Keung, An integration test order strategy to consider control coupling, IEEE Trans. Softw. Eng. 47 (2021) 1350–1367. https://doi.org/10.1109/TSE.2019.2921965 Crossref, Web of Science, Google Scholar