Skip to main content
SpringerLink
Log in

Study of an Improved Genetic Algorithm for Multiple Paths Automatic Software Test Case Generation

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10385))

Included in the following conference series:

Abstract

Automatic generation of test case is an important means to improve the efficiency of software testing. As the theoretical and experimental base of the existing heuristic search algorithm, genetic algorithm shows great superiority in test case generation. However, since most of the present fitness functions are designed by a single target path, the efficiency of the generating test case is relatively low. In order to cope with this problem, this paper proposes an efficiency genetic algorithm by using a novel fitness function. By generating multiple test cases to cover multiple target paths, this algorithm needs less iterations hence exhibits higher efficiency comparing to the existing algorithms. The simulation results have also shown that the proposed algorithm is high path coverage and high efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mansour, N., Salame, M.: Data generation for path testing. Softw. Qual. J. 12(2), 121–136 (2004)

    Article  Google Scholar 

  2. Chen, Y., Zhong, Y.: Automatic path oriented test data generation using a multi population genetic algorithm. In: Proceedings of the 4th International Conference on Natural Computation, pp. 566–570. IPICNC, Jinan, China (2008)

    Google Scholar 

  3. Ahmed, M.A., Hermadi, I.: GA-based multiple paths test data generator. Comput. Oper. Res. 35, 3107–3124 (2008)

    Article  Google Scholar 

  4. Gong, D.W., Zhang, Y.: Novel evolutionary generation approach to test data for multiple paths coverage. Acta Electron. Sin. 38(6), 1299–1304 (2010)

    MathSciNet  Google Scholar 

  5. Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in Genetic Algorithm. IEEE Trans. Syst. Man Cybern. 24(4), 656–667 (1994)

    Article  Google Scholar 

  6. Gong, D.W., Yao, X.J., Zhang, Y.: Evolution Theory and Application for Testing Data Generation, 1st edn, pp. 8–32. Science Press, Beijing (2014)

    Google Scholar 

  7. Sthamer, H.H.: The automatic generation of software test data using genetic algorithms. Ph.D. thesis. University of Glamorgan, Pontyprid, Wales, UK, pp. 25–48 (1995)

    Google Scholar 

Download references

Acknowledgments

This paper is supported by the National Natural Science Foundation of China (Grant No. 61300169) and the Natural Science Foundation of Education Department of Anhui province (Grant No. KJ2016A257).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Erzhou Zhu, Chenglong Yao & Feng Liu

  2. School of Economic and Technical, Anhui Agricultural University, Hefei, 230011, China

    Zhujuan Ma

Authors
  1. Erzhou Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenglong Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhujuan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Erzhou Zhu or Feng Liu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zhu, E., Yao, C., Ma, Z., Liu, F. (2017). Study of an Improved Genetic Algorithm for Multiple Paths Automatic Software Test Case Generation. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61824-1_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61823-4

  • Online ISBN: 978-3-319-61824-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation