In the realm of software testing, one of the most critical challenges faced by development teams is the limitation of resources and time. As software systems grow in complexity and size, the number of test cases increases significantly, making it impractical to re-execute the entire suite of tests in each testing cycle. Consequently, there arises a need for effective strategies to select and prioritize test cases in a way that ensures the most valuable and error-prone parts of the software are tested early. Prioritizing test cases not only accelerates the detection of defects but also enhances the efficiency of the testing process by focusing efforts on areas with higher potential for failure. In this study, two powerful nature-inspired metaheuristic algorithms Cuckoo Search Optimization Algorithm and Gravitational Search Algorithm are employed to address the problem of test case prioritization. These algorithms are used to prioritize test cases based on coverage criteria, particularly aiming for maximum fault detection coverage. By optimizing the sequence of test execution, the proposed method improves both the fault detection rate and the overall effectiveness of the testing process. This approach contributes to the timely identification of critical defects and supports faster and more reliable software releases.
Chi, J., Wang, Q., Yang, Z., & Li, X. (2020). Relation-based test case prioritization for regression testing. Journal of Systems and Software, 163, 110539. https://doi.org/10.1016/j.jss.2020.110539
Lima, J. A. P., & Vergilio, S. R. (2020). Test case prioritization in continuous integration environments: A systematic mapping study. Information and Software Technology, 121, 106268. https://doi.org/10.1016/j.infsof.2020.106268
Liu, Y., Li, J., Zhang, L., & Zhou, J. (2023). More precise regression test selection via reasoning about semantics-modifying changes. Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. https://doi.org/10.1145/3597926.3598086
Khaleel, S. I., & Anan, R. (2023). A review paper: Optimal test cases for regression testing using artificial intelligent techniques. International Journal of Electrical & Computer Engineering, 13(2), 1803–1816. https://doi.org/10.11591/ijece.v13i2.pp1803-1816
Torbunova, A., Strandberg, P. E., & Porres, I. (2024). Dynamic test case prioritization in industrial test result datasets. arXiv preprint arXiv:2402.02925. https://doi.org/10.1145/3644032.3644452
Sebastian, A., Naseem, H., & Catal, C. (2024). Unsupervised machine learning approaches for test suite reduction. Applied Artificial Intelligence, 38(1), 2322336. https://doi.org/10.1080/08839514.2024.2322336
Pan, R., Gao, J., Deng, Y., & Zhang, Z. (2022). Test case selection and prioritization using machine learning: A systematic literature review. Empirical Software Engineering, 27(2), 29. https://doi.org/10.1007/s10664-021-10066-6
Luo, Q., Xu, Y., Liu, Y., Yang, B., & Luo, J. (2020). Automated visual defect detection for flat steel surface: A survey. IEEE Transactions on Instrumentation and Measurement, 69(3), 626–644. https://doi.org/10.1109/TIM.2019.2963555
Qasim, M., Alenezi, M., & Mahmoud, S. A. (2021). Test case prioritization techniques in software regression testing: An overview. International Journal of Advanced and Applied Sciences, 8(5), 107–121. https://doi.org/10.21833/ijaas.2021.05.012
Marois, A., Mattei, N., Pesant, G., & Rossi, F. (2021). Evaluation of evolutionary algorithms under frugal learning constraints for online policy capturing. 2021 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). IEEE. https://doi.org/10.1109/CogSIMA51574.2021.9475930
Raimond, K., & Lovesum, J. (2019). A novel approach for automatic remodularization of software systems using extended ant colony optimization algorithm. Information and Software Technology, 114, 107–120. https://doi.org/10.1016/j.infsof.2019.06.002
Bhanushali, A., Gupta, K., & Sharma, M. (2024). Innovative approaches for machine-driven software testing using neural networks. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 724–732.
Mistry, J. (2024). Impact of model selection on pulmonary effusion diagnosis using prediction analysis algorithms. Journal of Xidian University, 18(1), 611–618.
Seyyedabbasi, A. (2023). A reinforcement learning-based metaheuristic algorithm for solving global optimization problems. Advances in Engineering Software, 178, 103411. https://doi.org/10.1016/j.advengsoft.2023.103411
Hernández-Sabaté, A., Albarracín, L., & Sánchez, F. J. (2020). Graph-based problem explorer: A software tool to support algorithm design learning while solving the salesperson problem. Mathematics, 8(9), 1595. https://doi.org/10.3390/math8091595
Asaad, M. M. O. F., Wahid, J., & Rahmat, A. R. (2024). Employing artificial bee colony algorithm to optimize the artificial neural network in heart disease prediction. AIP Conference Proceedings, 2895(1). AIP Publishing. https://doi.org/10.1063/5.0192144
Ramesh, R., & Karthic, M. J. (2024). Optimizing cryptocurrency price prediction: A hybrid approach with resilient stochastic clustering and gravitational search algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 239–248.
Momeni, E., Momeni, R., & Sadrekarimi, A. (2021). An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures. Transportation Geotechnics, 26, 100446. https://doi.org/10.1016/j.trgeo.2020.100446
Al-Abaji, M. A. (2021). Cuckoo search algorithm: Review and its application. Tikrit Journal of Pure Science, 26(2), 137–144. https://doi.org/10.25130/tjps.v26i2.130
Rath, P. K., Singh, A., Tripathy, B. K., & Satapathy, S. C. (2024). CSOFS: Feature selection using cuckoo search optimization algorithm for software fault detection. 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC). IEEE. https://doi.org/10.1109/ESIC60604.2024.10481641
Meydani,S. and Sadeghi,F. (2024). Increasing Error Detection in Software Testing Using Cuckoo Algorithm and Gravity Search Algorithm. Transactions on Machine Intelligence, 7(3), 208-222. doi: 10.47176/TMI.2024.208
MLA
Meydani,S. , and Sadeghi,F. . "Increasing Error Detection in Software Testing Using Cuckoo Algorithm and Gravity Search Algorithm", Transactions on Machine Intelligence, 7, 3, 2024, 208-222. doi: 10.47176/TMI.2024.208
HARVARD
Meydani S., Sadeghi F. (2024). 'Increasing Error Detection in Software Testing Using Cuckoo Algorithm and Gravity Search Algorithm', Transactions on Machine Intelligence, 7(3), pp. 208-222. doi: 10.47176/TMI.2024.208
CHICAGO
S. Meydani and F. Sadeghi, "Increasing Error Detection in Software Testing Using Cuckoo Algorithm and Gravity Search Algorithm," Transactions on Machine Intelligence, 7 3 (2024): 208-222, doi: 10.47176/TMI.2024.208
VANCOUVER
Meydani S., Sadeghi F. Increasing Error Detection in Software Testing Using Cuckoo Algorithm and Gravity Search Algorithm. Trans. Mach. Intell., 2024; 7(3): 208-222. doi: 10.47176/TMI.2024.208
