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Generating Optimal Test Case Generation Using Shuffled Shepherd Flamingo Search Model

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Abstract

Software testing is considered as the basic procedure and it is genuinely supportive for several software developers. Developing an automatic test case generation helps the software professionals to conserve more time. In recent years, with the aim of saving cost and time, majority of the software is delivered without sufficient testing resulting in revenue loss. Simultaneously, the testing cost minimizes with the diminution of testing time. To overcome the above-mentioned limitations, this paper proposes a shuffled shepherd flamingo search (S2FS) approach for an optimized and automatic test case generation with minimum execution time. The S2FS approach is an integration of two different metaheuristic algorithms namely the shuffled shepherd optimization algorithm and flamingo search optimization (FSO) algorithm. The significant intention of the proposed approach is to evaluate the efficiency and effectiveness thereby enhancing the generation of test cases. In addition to this, this paper also determines the efficiency of the proposed technique based on ATM operation with respect to total number of test case generations. The test cases of ATM are provided as an input to the proposed S2FS approach to determine an optimal test case. Finally, the experimental evaluations and comparative analysis are performed to determine the effectiveness of the proposed technique.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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References

  1. Saju Sankar VC (2020) An ant colony optimization algorithm based automated generation of software test cases. In: International Conference on Swarm Intelligence. Springer, Cham, pp 231–239

  2. Pandey A, Banerjee S (2021) Test suite optimization using chaotic firefly algorithm in software testing. In: Research Anthology on Recent Trends, Tools, and Implications of Computer Programming. IGI Global, pp 722–739

  3. Sureshkumar VS, Chandrasekar A (2013) Fuzzy-GA optimized multi-cloud multi-task scheduler for cloud storage and service applications. Int J Sci Eng Res 4(3):1–7

    Google Scholar 

  4. Sundararaj V (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325–345

    Article  Google Scholar 

  5. Jose J, Gautam N, Tiwari M, Tiwari T, Suresh A, Sundararaj V, Rejeesh MR (2021) An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomed Signal Proc Control 66:102480

    Article  Google Scholar 

  6. Zhang N, Jin Y, Tu X, Dong L, Bao X (2020) Combinatorial test case generation method based on improved particle swarm optimization algorithm. Softw Eng Appl 9:148

    Google Scholar 

  7. Hekmatnejad M, Hoxha B, Fainekos G (2020) Search-based test-case generation by monitoring responsibility safety rules. In: 2020 IEEE 23rd International conference on intelligent transportation systems (ITSC). IEEE, pp 1–8

  8. Raamesh L, Jothi S, Radhika S (2022) Enhancing software reliability and fault detection using hybrid brainstorm optimization-based LSTM model. IETE J Res, pp 1-15

  9. Raamesh L, Jothi S, Radhika S (2022) Test case minimization and prioritization for regression testing using SBLA-based adaboost convolutional neural network. J Supercomput, pp 1-25

  10. Khari M, Sinha A, Verdu E, Crespo RG (2020) Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimization. Soft Comput 24(12):9143–9160

    Article  Google Scholar 

  11. Damia A, Esnaashari M, Parvizimosaed M (2021) Automatic web-based software structural testing using an adaptive particle swarm optimization algorithm for test data generation. In: 2021 7th International Conference on Web Research (ICWR). IEEE, pp 282–286

  12. Du Y, Pan Y, Ao H, Alexander NO, Fan Y (2019) Automatic test case generation and optimization based on mutation testing. In: 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, pp 522–523

  13. Gupta N, Sharma A, Pachariya MK (2019) An insight into test case optimization: ideas and trends with future perspectives. IEEE Access 7:22310–22327

    Article  Google Scholar 

  14. Dai X, Gong W, Gu Q (2021) Automated test case generation based on differential evolution with node branch archive. Comput Ind Eng 156:107290

    Article  Google Scholar 

  15. Panigrahi SS, Sahoo PK, Sahu BP, Panigrahi A, Jena AK (2021). Model-driven automatic paths generation and test case optimization using hybrid FA-BC. In: 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, pp 263–268

  16. Shingadiya CJ (2021) Genetic algorithm for test suite optimization: an experimental investigation of different selection methods. Turk J Comput Math Educ (TURCOMAT) 12(3):3778–3787

    Article  Google Scholar 

  17. Khamprapai W, Tsai CF, Wang P, Tsai CE (2021) Performance of enhanced multiple-searching genetic algorithm for test case generation in software testing. Mathematics 9(15):1779

    Article  Google Scholar 

  18. Lakshminarayana P, SureshKumar TV (2021) Automatic generation and optimization of test case using hybrid cuckoo search and bee colony algorithm. J Intell Syst 30(1):59–72

    Article  Google Scholar 

  19. Sahoo RR, Ray M (2020) PSO based test case generation for critical path using improved combined fitness function. J King Saud Univ Comput Inf Sci 32(4):479–490

    Google Scholar 

  20. Boopathi M, Sujatha R, Kumar CS, Narasimman S (2017) Quantification of software code coverage using artificial bee colony optimization based on Markov approach. Arab J Sci Eng 42(8):3503–3519

    Article  MATH  Google Scholar 

  21. Gusev A, Ilin D, Nikulchev E (2020) The dataset of the experimental evaluation of software components for application design selection directed by the artificial bee colony algorithm. Data 5(3):59

    Article  Google Scholar 

  22. Sahin O, Akay B, Karaboga D (2021) Archive-based multi-criteria Artificial Bee Colony algorithm for whole test suite generation. Eng Sci Technol Int J 24(3):806–817

    Google Scholar 

  23. Boopathi M, Sujatha R, Kumar CS (2020) A tool for automatic generation of dd-graph using adjacency matrix for software testing. Life Cycl Reliab Saf Eng 9(4):379–387

    Article  Google Scholar 

  24. Philipp T, Roland V, Schweizer L (2021) Smoke test planning using answer set programming. Int J Interact Multimedia Artif Intell 6(5):57

    Google Scholar 

  25. Gupta S, Chug A (2021) An extensive analysis of machine learning-based boosting algorithms for software maintainability prediction. Int J Interact Multimedia Artif Intell 7(2):89

    Google Scholar 

  26. Mann M, Sangwan OP, Tomar P (2015) Hybrid test language processing based framework for test case optimization. CSI Trans ICT 3(2):71–81

    Article  Google Scholar 

  27. Bharathi M (2022) Hybrid particle swarm and ranked firefly metaheuristic optimization-based software test case minimization. Int J Appl Metaheur Comput 13(1):1–20

    Google Scholar 

  28. Sahoo RK, Mohapatra DP, Patra MR (2016) A firefly algorithm based approach for automated generation and optimization of test cases. Int J Comput Sci Eng 4(8):54–58

    Google Scholar 

  29. Kaveh A, Zaerreza A (2020) Size/layout optimization of truss structures using shuffled shepherd optimization method. Periodica Polytech Civil Eng 64(2):408–421

    Google Scholar 

  30. Kaveh A, Zaerreza A (2020) Shuffled shepherd optimization method: a new meta-heuristic algorithm. Eng Comput 37:2357

    Article  Google Scholar 

  31. Kaveh A (2021) Shuffled shepherd optimization algorithm. In: Kaveh A (ed) Advances in metaheuristic algorithms for optimal design of structures. Springer, Cham, pp 625–661

    Chapter  MATH  Google Scholar 

  32. AlappattJoe Prathap VPM (2021) Trust-based energy efficient secure multipath routing in MANET using LF-SSO and SH2E. Int J Comput Netw Appl 8(4):400–411

    Google Scholar 

  33. Zhiheng W, Jianhua L (2021) Flamingo search algorithm: a new swarm intelligence optimization algorithm. IEEE Access 9:88564–88582

    Article  Google Scholar 

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Authors and Affiliations

  1. St. Joseph’s College of Engineering, OMR, Chennai, 600119, India

    Lilly Raamesh

  2. School of Electrical and Electronics, Sathyabama Institute of Science and Technology, OMR, Chennai, 600119, India

    S. Radhika

  3. Department of CSE, St. Joseph’s College of Engineering, OMR, Chennai, 600119, India

    S. Jothi

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  1. Lilly Raamesh
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  2. S. Radhika
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  3. S. Jothi
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Correspondence to Lilly Raamesh.

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Raamesh, L., Radhika, S. & Jothi, S. Generating Optimal Test Case Generation Using Shuffled Shepherd Flamingo Search Model. Neural Process Lett 54, 5393–5413 (2022). https://doi.org/10.1007/s11063-022-10867-w

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