Skip to main content
SpringerLink
Log in

A Comprehensive Comparison of Ant Colony and Hybrid Particle Swarm Optimization Algorithms Through Test Case Selection

  • Conference paper
  • First Online:
Data Engineering and Intelligent Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 542 ))

Abstract

The focus of this paper is towards comparing the performance of two metaheuristic algorithms, namely Ant Colony and Hybrid Particle Swarm Optimization. The domain of enquiry in this paper is Test Case Selection, which has a great relevance in software engineering and requires a good treatment for the effective utilization of the software. Extensive experiments are performed using the standard flex object from SIR repository. Experiments are conducted using Matlab, where Execution time and Fault Coverage are considered as quality measure, is reported in this paper which is utilized for the analysis. The underlying motivation of this paper is to create awareness in two aspects: Comparing the performance of metaheuristic algorithms and demonstrating the significance of test case selection in software engineering.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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. Mirarab, S., Akhlaghi, S., Tahvildari, L.: Size-constrained regression test case selection using multi-criteria optimization. IEEE Trans. Softw. Eng. 38(4), 936–956 (2012)

    Article  Google Scholar 

  2. Rothermel, G., Harrold, M.J., Dedhia, J.: Regression test selection for C++ software. Softw. Test. Verif. Reliab. 10(2), 77–109 (2000)

    Article  Google Scholar 

  3. Yoo, S., Harman, M.: Regression testing minimization, selection and prioritization: a survey. Softw. Test. Verif. Reliab. 22(2), 67–120 (2012)

    Article  Google Scholar 

  4. Mao, C.: Built-in regression testing for component-based software systems. In: 31st Annual International on Computer Software and Applications Conference, 2007. COMPSAC 2007, vol. 2, pp. 723–728. IEEE (2007)

    Google Scholar 

  5. Ali, A., Nadeem, A., Iqbal, M.Z.Z., Usman, M.: Regression testing based on UML design models. In: 13th Pacific Rim International Symposium on Dependable Computing, 2007. PRDC 2007, pp. 85–88. IEEE (2007)

    Google Scholar 

  6. Nagar, R., Kumar, A., Singh, G.P., Kumar, S.: Test case selection and prioritization using cuckoos search algorithm. In: International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), pp. 283–288, IEEE (2015)

    Google Scholar 

  7. Jeffrey, D., Gupta, N.: Experiments with test case prioritization using relevant slices. J. Syst. Softw. 81(2), 196–221 (2008)

    Article  Google Scholar 

  8. Kaur, A., Goyal, S.: A bee colony optimization algorithm for fault coverage based regression test suite prioritization. Int. J. Adv. Sci. Technol. 29, 17–30 (2011)

    Google Scholar 

  9. Kumar, M., Sharma, A., Kumar, R.: An empirical evaluation of a three-tier conduit framework for multifaceted test case classification and selection using fuzzy-ant colony optimisation approach. Softw. Pract. Exp. 45(7), 949–971 (2015)

    Google Scholar 

  10. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  11. Liu, B., Wang, L., Jin, Y.H.: An effective hybrid pso-based algorithm for flow shop scheduling with limited buffers. Comput. Oper. Res. 35(9), 2791–2806 (2008)

    Article  MATH  Google Scholar 

  12. Apostolopoulos, T., Vlachos, A.: Application of the firefly algorithm for solving the economic emissions load dispatch problem. Int. J. Comb. (2011)

    Google Scholar 

  13. Chang, X., Yi, P., Zhang, Q.: Key frames extraction from human motion capture data based on hybrid particle swarm optimization algorithm. In: Recent Developments in Intelligent Information and Database Systems, pp. 335–342. Springer International Publishing (2016)

    Google Scholar 

  14. Wang, G.G., Gandomi, A.H., Alavi, A.H., Deb, S.: A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput. Appl. 27(4), 989–1006 (2016)

    Article  Google Scholar 

  15. Girish, B.S.: An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem. Appl. Soft. Comput. 44, 200–221 (2016)

    Article  Google Scholar 

  16. Cui, G., Qin, L., Liu, S., Wang, Y., Zhang, X., Cao, X.: Modified PSO algorithm for solving planar graph colouring problem. Prog. Nat. Sci. 18(3), 353–357 (2008)

    Article  Google Scholar 

  17. Kakkar, M., Jain, S.: Feature selection in software defect prediction: a comparative study. In: 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 658–663. IEEE (2016)

    Google Scholar 

  18. Tayarani, N.M.H., Yao, X., Xu, H.: Meta-heuristic algorithms in car engine design: a literature survey. IEEE Trans. Evol. Comput. 19(5), 609–629 (2015)

    Article  Google Scholar 

  19. Agrawal, A.P., Kaur, A.: A comparative analysis of memory using and memory less algorithms for quadratic assignment problem. In: 2014 5th International Conference on Confluence the Next Generation Information Technology Summit (Confluence), pp. 815–820. IEEE (2014)

    Google Scholar 

  20. Do, H., Elbaum, S., Rothermel, G.: Supporting controlled experimentation with testing techniques: an infrastructure and its potential impact. Empir. Softw. Eng. 10(4), 405–435 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University, Noida, Uttar Pradesh, India

    Arun Prakash Agrawal

  2. University School of Information and Communication Technology, GGSIP University, New Delhi, India

    Arvinder Kaur

Authors
  1. Arun Prakash Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arvinder Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arun Prakash Agrawal .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  4. DVR and Dr. HS MIC College of Technology, Kanchikacherla, Andhra Pradesh, India

    B. Janakiramaiah

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Agrawal, A.P., Kaur, A. (2018). A Comprehensive Comparison of Ant Colony and Hybrid Particle Swarm Optimization Algorithms Through Test Case Selection. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542 . Springer, Singapore. https://doi.org/10.1007/978-981-10-3223-3_38

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3223-3_38

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3222-6

  • Online ISBN: 978-981-10-3223-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation