Application of Gradient-Based Optimizer for Development of Enhanced Fitness Function with Critical Path Weights for Generating Test Data
Keywords:
Normalized branch distance; Gradient-based method; Test case generation; Optimization; Approach level; Software test case; Combined fitness functionAbstract
The testing phases are generally resource-intensive, which initially includes the development of test cases followed by their generation. Such phases significantly impact the entire testing process in terms of their effectiveness and efficiency. The identification of an efficient method for the further generation of test cases that could ensure the achievement of maximum path coverage with the limited available testing resources is the primary objective of this paper. To accomplish the above-mentioned task, the key component is the selection of the appropriate fitness function, which will play a vigorous role in the process of optimization. The proposed study in the paper introduces an enhanced combined fitness function that would influence the optimization of performance. The enhanced fitness function is also proposed to incorporate the weights for the critical paths, which would further allow the optimizer to prioritize the coverage of these paths while reducing the overall time essential for the generation of test cases. The criteria selected to assign weights to critical paths further collaborate with normalized branch distance (NBD) functions and approach level (AL). A gradient-based optimizer (GBO) is also employed for the generation of test cases, which is expected to result in impressive outcomes. To generate the test cases systematically, it is also combined with the refined fitness function. The experiments further reveal that the approach being proposed in this paper surpasses current state-of-the-art approaches in various aspects, such as the execution time required, the number of iterations required, and the average number of test instances generated.
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S. Carbas, A. Toktas, and D. Ustun, “Introduction and Overview: Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications”, Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications, pp. 1-9, 2021. https://doi.org/10.1007/978-981-33-6773-9_1
Y. Chen, Y. Zhong, T. Shi, and J. Liu, “Comparison of two fitness functions for GA-based path-oriented test data generation”, Fifth International Conference on Natural Computation, vol. 4, pp. 177-181, Aug 2009. IEEE. https://doi.org/10.1109/ICNC.2009.235
M. Harman, S. A. Mansouri, and Y. Zhang, “Search-based software engineering: Trends, techniques and applications”, ACM Computing Surveys (CSUR), vol. 45(1), pp. 1-61, 2012. https://doi.org/10.1145/2379776.2379787
V. Tomar, and M. Bansal, "Software Testing and Test Case Optimization: Concepts and Trends." In Electronic Systems and Intelligent Computing: Proceedings of ESIC 2021, pp. 525-532. Singapore: Springer Nature Singapore, 2022.
D. Garg and P. Garg, "Basis path testing using SGA & HGA with ExLB fitness function." Procedia Computer Science”, 70 2015, pp. 593-602.
N. Nayak, and D. P. Mohapatra, “Automatic test data generation for data flow testing using particle swarm optimization”, In Contemporary Computing: Third International Conference, Part II vol. 3, pp. 1-12, Aug 2010. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-14825-5_1
D. B. Mishra, R. Mishra, K. N. Das, and A. A. Acharya, “Test case generation and optimization for critical path testing using genetic algorithm”, In Soft Computing for Problem Solving: SocProS 2017, Vol. 2 (pp. 67-80), 2019. Springer Singapore. https://doi.org/10.1007/978-981-13-1595-4_6
M. Khari, P. Kumar, “An extensive evaluation of search-based software testing: a review”, Soft Computing, vol. 23, pp. 1933-1946, 2019. https://doi.org/10.1007/s00500-017-2906-y
M. S. Daoud, M. Shehab, H. M. Al-Mimi, L. Abualigah, R.A. Zitar, and M. K. Y. Shambour, “Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications”, Archives of Computational Methods in Engineering, vol. 30(4), pp. 2431-2449, May 2023. https://doi.org/10.1007/s11831-022-09872-y
A. M. Altaie, T. M. Tawfeeq, and M. G. Saeed, “Automated Test Suite Generation Tool based on GWO Algorithm”, Webology, vol. 19(1), pp. 3835-3849, 2022. https://doi.org/10.14704/WEB/V19I1/WEB19252
R. K. Sahoo, S. Satpathy, S. Sahoo, and A. Sarkar, “Model driven test case generation and optimization using adaptive cuckoo search algorithm”, Innovations in Systems and Software Engineering, vol. 18(2), pp. 321-331, 2022. https://doi.org/10.1007/s11334-020-00378-z
A. Goli, H. Khademi-Zare, R. Tavakkoli-Moghaddam, A. Sadeghieh, M. Sasanian, and R. Malekalipour Kordestanizadeh, “An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study”, Network: computation in neural systems, vol. 32(1), pp. 1-35, 2021. https://doi.org/10.1080/0954898X.2020.1849841
J. Lu, W. Xie, and H. Zhou, “Combined fitness function-based particle swarm optimization algorithm for system identification”, Computers & Industrial Engineering, vol. 95, pp. 122-134, 2016. https://doi.org/10.1016/j.cie.2016.03.007
N. Jatana, and B. Suri, “An improved crow search algorithm for test data generation using search-based mutation testing”, Neural Processing Letters, vol. 52, pp. 767-784, 2020. https://doi.org/10.1007/s11063-020-10288-7
M. Khari, A. Sinha, E. Verdu, and R. G. Crespo, “Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimization”, Soft Computing, vol. 24(12), pp. 9143-9160, 2020. https://doi.org/10.1007/s00500-019-04444-y
R. R. Sahoo, and M. Ray, “PSO based test case generation for critical path using improved combined fitness function”, Journal of King Saud University-Computer and Information Sciences, vol. 32(4), pp. 479-490, 2020. https://doi.org/10.1016/j.jksuci.2019.09.010
A. Faramarzi, M. Heidarinejad, B. Stephens, and S. Mirjalili, “Equilibrium optimizer: A novel optimization algorithm”, Knowledge-Based Systems, vol. 191, p. 105190, 2020. https://doi.org/10.1016/j.knosys.2019.105190
D. B. Mishra, R. Mishra, K. N. Das, and A. A. Acharya, “Test case generation and optimization for critical path testing using genetic algorithm”, In Soft Computing for Problem Solving: SocProS 2017, vol. 2, pp. 67-80, 2019. Springer Singapore. https://doi.org/10.1007/978-981-13-1595-4_6
H. Huang, F. Liu, X. Zhuo, and Z. Hao, “Differential evolution based on self-adaptive fitness function for automated test case generation”, IEEE Computational Intelligence Magazine, vol. 12(2), pp. 46-55, 2017. https://doi.org/10.1109/MCI.2017.2670462
M. Khari, P. Kumar, D. Burgos, and R. G. Crespo, “Optimized test suites for automated testing using different optimization techniques”, Soft Computing, vol. 22, pp. 8341-8352, 2018. https://doi.org/10.1007/s00500-017-2780-7
K. Solanki, Y. Singh, and S. Dalal, “Experimental analysis of m-ACO technique for regression testing”, Indian Journal of Science and Technology, vol. 9(30), pp. 1-7, 2016. https://doi.org/10.17485/ijst/2016/v9i30/86588
S, Jiang, J. Chen, Y. Zhang, J. Qian, R. Wang, and M. Xue, “Evolutionary approach to generating test data for data flow test”, IET Software, vol. 12(4), pp. 318-323, 2018. https://doi.org/10.1049/iet-sen.2018.5197
I. Ahmadianfar, O. Bozorg-Haddad, and X. Chu, “Gradient-based optimizer: A new metaheuristic optimization algorithm”, Information Sciences, vol. 540, pp. 131-159, 2020. https://doi.org/10.1016/j.ins.2020.06.037
C. Henard, M. Papadakis, M. Harman, Y. Jia, and Y. Le Traon, “Comparing white-box and black-box test prioritization”, In Proceedings of the 38th International Conference on Software Engineering, pp. 523-534, May 2016. https://doi.org/10.1145/2884781.2884791
V. Tomar, M. Bansal, and P. Singh, "Regression Testing Approaches, Tools, and Applications in Various Environments." In 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST), pp. 1-6. IEEE, 2022.
C. Ebert, J. Cain, G. Antoniol, S. Counsell, and P. Laplante, “Cyclomatic complexity”, IEEE software, vol. 33(6), pp. 27-29, 2016. https://doi.org/10.1109/MS.2016.147
A. R. Álvares, J. N. Amaral, and F. M. Q. Pereira, “Instruction visibility in SPEC CPU2017”, Journal of Computer Languages, vol. 66, p. 101062, Oct 2021. https://doi.org/10.1016/j.cola.2021.101062
N. Ukić, J. Maras, and L. Šerić, “The influence of cyclomatic complexity distribution on the understandability of xtUML models”, Software Quality Journal, vol. 26, pp. 273-319, 2018. https://doi.org/10.1007/s11219-016-9351-5
M. Harman, Y. Jia, and Y. Zhang, “Achievements, open problems and challenges for search-based software testing”, 8th International Conference on Software Testing, Verification and Validation (ICST), pp. 1-12, April 2015, IEEE. https://doi.org/10.1109/ICST.2015.7102580
R. Mall, “Fundamentals of software engineering”, PHI Learning Pvt. Ltd., 2018.
K. Chen, F. Y. Zhou, and X. F.Yuan, “Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection”, Expert Systems with Applications, vol. 128, pp. 140-156, 2019. https://doi.org/10.1016/j.eswa.2019.03.039.
V. Tomar, M. Bansal, and P. Singh, "Metaheuristic Algorithms for Optimization: A Brief Review." Engineering Proceedings 59, no. 1 (2024): 238.
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