Multi-objective test case prioritization based on multi-population cooperative particle swarm optimization

doi:10.19682/j.cnki.1005-8885.2020.0003

中国邮电高校学报(英文) ›› 2020, Vol. 27 ›› Issue (1): 38-50.doi: 10.19682/j.cnki.1005-8885.2020.0003

• Computer Applied Technology • 上一篇下一篇

Multi-objective test case prioritization based on multi-population cooperative particle swarm optimization

王红熳¹,李晋忠¹,邢颖²,周晓光³

1. 北京市海淀区西土城路10号北京邮电大学
2. 北京邮电大学自动化学院
3. 北京邮电大学

收稿日期:2019-05-15 修回日期:2019-12-11 出版日期:2020-02-28 发布日期:2020-02-28
通讯作者: 李晋忠 E-mail:lijinzhong2051@163.com
基金资助:
国家自然科学基金

Multi-objective test case prioritization based on multi-population cooperative particle swarm optimization

Received:2019-05-15 Revised:2019-12-11 Online:2020-02-28 Published:2020-02-28
Supported by:
National Natural Science Foundation of China

摘要/Abstract

摘要： Test case prioritization (TCP) technique is an efficient approach to improve regression testing activities. With the continuous improvement of industrial testing requirements, traditional single-objective TCP is limited greatly, and multi-objective test case prioritization (MOTCP) technique becomes one of the hot topics in the field of software testing in recent years. Considering the problems of traditional genetic algorithm (GA) and swarm intelligence algorithm in solving MOTCP problems, such as falling into local optimum quickly and weak stability of the algorithm, a MOTCP algorithm based on multi-population cooperative particle swarm optimization (MPPSO) was proposed in this paper. Empirical studies were conducted to study the influence of iteration times on the proposed MOTCP algorithm, and compare the performances of MOTCP based on single-population particle swarm optimization (PSO) and MOTCP based on non-dominated sorting genetic algorithm II (NSGA-II) with the MOTCP algorithm proposed in this paper. The results of experiments show that the test case prioritization algorithm based on MPPSO has stronger global optimization ability, is not easy to fall into local optimum, and can solve the MOTCP problem better than test case prioritization algorithm based on the single-population PSO and NSGA-II.

关键词: regression testing, test case prioritization, multi-population cooperative particle swarm optimization, multi-objective optimization

Abstract: Test case prioritization (TCP) technique is an efficient approach to improve regression testing activities. With the continuous improvement of industrial testing requirements, traditional single-objective TCP is limited greatly, and multi-objective test case prioritization (MOTCP) technique becomes one of the hot topics in the field of software testing in recent years. Considering the problems of traditional genetic algorithm (GA) and swarm intelligence algorithm in solving MOTCP problems, such as falling into local optimum quickly and weak stability of the algorithm, a MOTCP algorithm based on multi-population cooperative particle swarm optimization (MPPSO) was proposed in this paper. Empirical studies were conducted to study the influence of iteration times on the proposed MOTCP algorithm, and compare the performances of MOTCP based on single-population particle swarm optimization (PSO) and MOTCP based on non-dominated sorting genetic algorithm II (NSGA-II) with the MOTCP algorithm proposed in this paper. The results of experiments show that the test case prioritization algorithm based on MPPSO has stronger global optimization ability, is not easy to fall into local optimum, and can solve the MOTCP problem better than test case prioritization algorithm based on the single-population PSO and NSGA-II.

Key words: regression testing, test case prioritization, multi-population cooperative particle swarm optimization, multi-objective optimization

中图分类号:

TP311.5

参考文献

1. Elbaum S, Rotherel G, Penix J. Techniques for improving regression testing in continuous integration development environments. Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE’14), Nov 16-21, 2014, Hong Kong, China. New York, NY, USA: ACM, 2014: 235-245 2. Elbaum S, Malishevsky A G, Rotherel G. Test case prioritization: A family of empirical studies. IEEE Transactions on Software Engineering, 2002, 28(2): 159-182 3. Khatibsyarbini M, Isa M A, Jawawi D N A, et al. Test case prioritization approaches in regression testing: A systematic literature review. Information and Software Technology, 2018, 93: 74-93 4. Parejo J A, Sanchez A B, Segura S, et al. Multi-objective test case prioritization in highly configurable systems: A case study. Journal of Systems and Software, 2016, 122: 287-310 5. Sun J Z, Chen J M, Wang G. Multi-objective test case prioritization based on epistatic particle swarm optimization. International Journal of Performability Engineering, 2018, 14(10): 2441-2448 6. Yu H, Wang Y J, Chen Q, et al. Multi-objective particle swarm optimization based on multi-population dynamic cooperation. Electronic Science and Technology, 2019, (10): 28-33 (in Chinese) 7. Wong W E, Horgan J R, London S, et al. A study of effective regression testing in practice. Proceedings of the 8th International Symposium on Software Reliability Engineering, Nov 2-5, 1997, Albuquerque, NM, USA. Piscataway, NJ, USA: IEEE, 1997: 264-174 8. Rothermel G, Untch R H, Chu C Y, et al. Test case prioritization: An empirical study. Proceedings of the 1999 IEEE International Conference on Software Maintenance (ICSM'99), Aug 30-Sept 3, 1999, Oxford, UK. Piscataway, NJ, USA: IEEE,1999: 179-188 9. Rothermel G, Untch R H, Chu C Y, et al. Prioritizing test cases for regression testing. IEEE Transactions on Software Engineering, 2001, 27(10): 929-948 10. Li Z, Harman M, Hierons R M. Search algorithms for regression test case prioritization. IEEE Transactions on Software Engineering, 2007, 33(4): 225-237 11. Gladston A, Nehemiah K, Narayanasamy P, et al. Test case prioritization for regression testing using immune operator. The International Arab Journal of Information Technology, 2016, 13(6): 686-692 12. Gao D D, Guo X Y, Zhao L. Test case prioritization for regression testing based on ant colony optimization. Proceedings of the 6th IEEE International Conference on Software Engineering and Service Science (ICSESS’15), Sept 23-25, 2015, Beijing, China. Piscataway, NJ, USA: IEEE, 2015: 275-279 13. Mishra D B, Mishra R, Das K N, et al. A systematic review of software testing using evolutionary techniques. Advances in Intelligent Systems and Computing: Proceedings of the 6th International Conference on Soft Computing for Problem Solving (SocProS’16): Vol 1, Dec 23-24, Patiala, India. AISC 546. Singapore : Springer, 2017: 174-184 14. Yoo S, Harman M. Pareto efficient multi-objective test case selection. Proceedings of the 2007 International Symposium on Software Testing and Analysis, Jul 9-12, 2007, London, UK. New York, NY, USA: ACM, 2007: 140-150 15. Bian Y, Yuan F, Guo J X, et al. Multi-objective test suite prioritization on CPU+GPU architecture. Journal of Software, 2016, 27(4): 943-954 (in Chinese) 16. Xing X, Shang Y, Zhao R L, et al. Pheromone updating strategy of ant colony algorithm for multi-objective test case prioritization. Journal of Computer Applications, 2016, 36(9): 2497-2502 (in Chinese) 17. Zhang N, Yao L, Bao X A, et al. Multi-objective optimization based on-line adjustment strategy of test case prioritization. Journal of Software, 2015, 26(10): 2451-2464 (in Chinese) 18. Tyagi M, Malhotra S. Test case prioritization using multi objective particle swarm optimizer. Proceedings of the 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT’14), Jul 12-13, 2014, Ajmer, India. Piscataway, NJ, USA: IEEE, 2014: 390-395 19. Chen Y F, Li Z, Zhao R L. Applying PSO to multi-objective test cases prioritization. Computer Science, 2014, 41(5): 72-77 (in Chinese) 20. Coello C A, Lechuga M S. MOPSO: A proposal for multiple objective particle swarm optimization (CEC'02). Proceedings of the 2002 Congress on Evolutionary Computation, May 12-17, 2002, Honolulu, HI, USA. Piscataway, NJ, USA: IEEE, 2002: 1051-1056 21. Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of the International Conference on Neural Networks (ICNN'95), Nov 27-Dec 1, 1995, Perth, Australia. Piscataway, NJ, USA: IEEE, 1995: 1942-1948 22. Liu J H, Yang R H, Sun S H. The analysis of binary particle swarm optimization. Journal of Nanjing University (Natural Science), 2011, 47(5): 504-514 (in Chinese) 23. Niu B, Zhu Y L, He X X. Multi-population cooperative particle swarm optimization. Proceedings of the 8th European Conference on Artificial Life, Sept 5-9, 2005, Canterbury, UK. Berlin, Germany: Springer, 2005: 874-883 24. Shi Y N, Li Z, Gong P. Multi-objective coevolutionary test case prioritization. Computer Science, 2015, 42(12): 124-129 (in Chinese) 25. Deb K, Pratap A, Agarwal S, et al. A fast and elitist multi objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197

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Multi-objective test case prioritization based on multi-population cooperative particle swarm optimization

Multi-objective test case prioritization based on multi-population cooperative particle swarm optimization

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