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Generating Software Test Data by Particle Swarm Optimization

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Book cover Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

Search-based method using meta-heuristic algorithms is a hot topic in automatic test data generation. In this paper, we develop an automatic test data generating tool named particle swarm optimization data generation tool (PSODGT). The PSODGT is characterized by the following two features. First, the PSODGT adopts the condition-decision coverage (C/DC) as the criterion of software testing, aiming to build an efficient test data set that covers all conditions. Second, the PSODGT uses a particle swarm optimization (PSO) approach to generate test data set. In addition, a new position initialization technique is developed for PSO. Instead of initializing the test data randomly, the proposed technique uses the previously-found test data that can reach the target condition as the initial positions so that the search speed of PSODGT can be further accelerated. The PSODGT is tested on four practical programs. Experimental results show that the proposed PSO approach is promising.

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References

  1. National Institute of Standards and Technology, “Then Economic Impacts of Inadequate Infrastructure for Software Testing,” Planning Report 02-3 (May 2002)

    Google Scholar 

  2. Myers, G.J., Sandler, C., Badgett, T.: The art of software testing. John Wiley & Sons (2011)

    Google Scholar 

  3. Díaz, E., Tuya, J., Blanco, R.: Automated Software Testing Using a Metaheuristic Technique Based on Tabu Search. In: Proc. 18th IEEE Int’l Conf. Automated Software Eng., pp. 310–313 (2003)

    Google Scholar 

  4. Michael, C.C., McGraw, G., Schatz, M.A.: Generating software test data by evolution. IEEE Trans. Software Eng. 27(12), 1085–1110 (2001)

    Article  Google Scholar 

  5. Bottaci, L.: Instrumenting Programs with Flag Variables for Test Data Search by Genetic Algorithm. In: Proc. Genetic and Evolutionary Computation Conf., pp. 1337–1342 (2002)

    Google Scholar 

  6. Li, A., Zhang, Y.-L.: Automatic Generating All-Path Test Data of a Program Based on PSO. In: WRI World Congress on Software Eng., pp. 189-193 (2009)

    Google Scholar 

  7. Windisch, A., Wappler, S., Wegener, J.: Applying Particle Swarm Optimization to Software Testing. In: Proc. 9th Ann. Genetic and Evolutionary Computation Conf., pp. 1121–1128 (2007)

    Google Scholar 

  8. Cui, H.-H., Chen, L., Zhu, B., Kuang, H.-L.: An Efficient Automated Test Data Generation Method. In: Int’l Conf. Measuring Technology and Mechatronics Automation, vol. 1, pp. 453–456 (2010)

    Google Scholar 

  9. Zhang, S., Zhang, Y., Zhou, H., He, Q.-Q.: Automatic Path Test Data Generation Based on GA-PSO. In: Proc. IEEE Int’l Conf. Intelligent Computing and Intelligent Systems, pp. 142–146 (2010)

    Google Scholar 

  10. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int’l Conf. Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  11. Frankl, P., Hamlet, D., Littlewood, B., Strigini, L.: Choosing a Testing Method to Deliver Reliability. In: Proc. Int’l Conf. Software Eng., pp. 68–78 (1997)

    Google Scholar 

  12. Chang, K.H., Cross II, J.H., Carlisle, W.H., Liao, S.-S.: A Performance Evaluation of Heuristics-Based Test Case Generation Methods for Software Branch Coverage. Int’l J. Software Eng. and Knowledge Eng. 6(4), 585–608 (1996)

    Article  Google Scholar 

  13. Clarke, L.A.: A System to Generate Test Data Symbolically and Execute Programs. IEEE Trans. Software Eng. 2(3), 215–222 (1976)

    Article  Google Scholar 

  14. Offutt, A.J.: An Integrated Automatic Test Data Generation System. J. Systems Integration 1, 391–409 (1991)

    Article  Google Scholar 

  15. Korel, B.: Automated Software Test Data Generation. IEEE Trans. Software Eng. 16(8), 870–879 (1990)

    Article  Google Scholar 

  16. Sofokleous, A.A., Andreou, A.S.: Automatic, evolutionary test data generation for dynamic software testing. J. Systems and Software 81(11), 1883–1898 (2008)

    Article  Google Scholar 

  17. Harman, M., McMinn, P.: A theoretical and empirical study of search-based testing: Local, global, and hybrid search. IEEE Trans. Software Eng. 36(2), 226–247 (2010)

    Article  Google Scholar 

  18. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proc. IEEE Int’l Conf. Evolutionary Computation, pp. 69–73 (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Ya-Hui Jia & Jun Zhang

  2. School of Advanced Computing, Sun Yat-sen University, Guangzhou, China

    Wei-Neng Chen & Jun Zhang

  3. Key Lab. Machine Intelligence and Advanced Computing, Ministry of Education, China

    Ya-Hui Jia, Wei-Neng Chen & Jun Zhang

  4. Engineering Research Center of Supercomputing Engineering Software, MOE, China

    Ya-Hui Jia, Wei-Neng Chen & Jun Zhang

  5. School of Computer Science, South China Normal University, Guangzhou, China

    Jing-Jing Li

Authors
  1. Ya-Hui Jia
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  2. Wei-Neng Chen
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  3. Jun Zhang
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  4. Jing-Jing Li
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Jia, YH., Chen, WN., Zhang, J., Li, JJ. (2014). Generating Software Test Data by Particle Swarm Optimization. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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