An Extended Adaptive Cuckoo Search Technique for Test Case Optimization

James Maina Mburu; John Ndia; Samson Munialo

doi:10.35377/saucis...1873936

An Extended Adaptive Cuckoo Search Technique for Test Case Optimization

Abstract

Model-driven test case production using Unified Modeling Language (UML) models has been widely explored to support early testing, often before system implementation. However, many existing techniques still struggle to produce optimal results efficiently, requiring a large number of iterations while achieving limited test coverage, as they rely on only a few UML models. To address these limitations, this study proposes an Extended Adaptive Cuckoo Search Technique (EACST), which builds and improves the Adaptive Cuckoo Search Algorithm (ACSA). Guided by the Design Science Research Methodology, the technique was conceptualized, developed, and evaluated through comparative experiments involving the Adaptive Cuckoo Search Algorithm (ACSA), the Enhanced Cuckoo Search Algorithm (ECSA), the hybrid Artificial Bee Colony and Simulated Annealing algorithm (ABC_SA), the Hybrid Graph-based Genetic Algorithm (HGGA), and the Differential Evolution algorithm with exponential crossover based on a learning strategy within the difference vector (DLS_DE). The initial evaluation was conducted using a single dataset to assess baseline performance under controlled conditions. The results demonstrate that EACST achieves optimal solutions using fewer iterations and reduced execution time compared to baseline techniques. Statistical validation using the Friedman and Wilcoxon tests further confirms these improvements, showing that EACST significantly outperforms ACSA, ECSA, ABC_SA, HGGA and DLS_DE. To ensure generalizability, a cross-system evaluation involving ten datasets was subsequently performed. The results show that EACST consistently generates a minimal test suite with maximum coverage, reduced effort, and fewer iterations compared to existing techniques. The proposed technique can be applied to a wide range of systems, including real-time environments such as Internet of Things systems and autonomous vehicles that utilize object-oriented paradigms. Future work will focus on developing hybrid approaches by integrating the proposed technique with Large Language Models and Reinforcement Learning to further enhance performance.

Keywords

Ethical Statement

This study did not involve any personal or sensitive data. It was based solely on system models from existing studies and has been cited and listed in the bibliography. Therefore, formal ethics committee approval was not required.

Thanks

This work has not been financially funded by any sponsor or institution. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

References

S. Tamizharasi, A. Ezhumalai, S. Remya Rose, P. Suresh, and Logesswari, “Bio-inspired approach for generating test data from user stories,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 2, pp. 412–419, 2021. doi: 10.17762/turcomat.v12i2.826.
S. Jiménez Gil, M. I. Capel, and G. Olea Olea, “Automatic test case generation from formal contracts,” Inf. Softw. Technol., vol. 172, Art. no. 107467, Aug. 2024, doi: 10.1016/j.infsof.2024.107467.
P. Jain and D. Soni, “A survey on generation of test cases using UML diagrams,” Proc. IC-ETITE, 2020. doi: 10.1109/ic-ETITE47903.2020.395.
R. Singh, “Automating test case generation for object-oriented systems using activity diagrams,” International Journal of Engineering and Computer Science, vol. 4, no. 9, pp. 14164–14171, 2015.
S. Saleem et al., “A UML-based approach towards test case generation and optimization,” KSII Trans. Internet Inf. Syst., vol. 18, no. 3, pp. 633–652, 2024, doi: 10.3837/tiis.2024.03.006.
R. K. Sahoo, D. P. Mohapatra, and M. R. Patra, “Model driven approach for test data optimization using activity diagram based on cuckoo search algorithm,” Int. J. Inf. Technol. Comput. Sci., vol. 9, no. 10, pp. 77–84, 2017, doi: 10.5815/ijitcs.2017.10.08.
M. A. Villanueva, C. M. Ching, and K. Mata, “ An enhancement of cuckoo search algorithm for optimal earthquake evacuation space allocation in Intramuros, Manila City," arXiv preprint arXiv:2502.13477,” 2025, doi: 10.48550/arXiv.2502.13477.
R. K. Sahoo, M. Derbali, H. Jerbi, D. V. Thang, P. P. Kumar, and S. Sahoo, “Test case generation from UML-diagrams using genetic algorithm,” Comput. Mater. Contin., vol. 67, no. 2, pp. 2321–2336, 2021, doi: 10.32604/cmc.2021.013014.

J. G. Tiwari and U. Suman, “HGGA-TCG model for optimized test case generation from UML diagrams,” in Proc. 2025 IEEE 5th Int. Conf. ICT in Business, Industry and Government (ICTBIG), 2025, doi: 10.1109/ICTBIG68706.2025.11323909.
A. Tamizharasi and P. Ezhumalai, “Genetic based crow search algorithm for test case generation,” Int. Trans. J. Eng. Manag. Appl. Sci. Technol., vol. 13, no. 4, 2022, doi: 10.14456/ITJEMAST.2022.74.
S. S. Panigrahi, P. K. Sahoo, B. P. Sahu, A. Panigrahi, and A. K. Jena, “Model-driven automatic path generation and test case optimization using hybrid FA-BC,” Proc. ESCI, 2021. doi: 10.1109/ESCI50559.2021.9396999.
J. Chen and Z. Meng, “An adaptive differential evolution algorithm with exponential crossover based on a learning strategy within the difference vector,” Swarm Evol. Comput., vol. 100, no. December 2025, p. 102247, 2026, doi: 10.1016/j.swevo.2025.102247.
S. Basri et al., “HABCSm: A Hamming Based t-way Strategy Based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation,” Int. J. Comput. Commun. Control, vol. 16, no. October, pp. 1–18, 2021.
A. Angelov and M. Lazarova, “Hybrid artificial bee colony algorithm for test case generation and optimization,” Algorithms, vol. 18, no. 10, Art. no. 668, Oct. 2025, doi: 10.3390/a18100668.
J. M. Mburu, J. G. Ndia, and S. W. Munialo, “Metaheuristic techniques for test case optimization: A systematic literature review,” International Journal of Software Engineering, vol. 12, no. 4, pp. 52–77, 2025
J. Cheng and Y. Xiong, “Multi-strategy adaptive cuckoo search algorithm for numerical optimization,” Artif. Intell. Rev., vol. 56, pp. 2031–2055, 2023, doi: 10.1007/s10462-022-10222-4.
R. Salgotra, U. Singh, S. Saha, and A. H. Gandomi, “Self-adaptive cuckoo search: Analysis and experimentation,” Swarm and Evolutionary Computation, vol. 60, 2021. doi: 10.1016/j.swevo.2020.100751.
R. K. Sahoo, S. Satpathy, S. Sahoo, and A. Sarkar, “Model-driven test case generation and optimization using adaptive cuckoo search algorithm,” Innov. Syst. Softw. Eng., vol. 18, pp. 321–331, 2022, doi: 10.1007/s11334-020-00378-z.
M. R. Reddy, M. L. R. Chandra, and R. Dilli, “Enhanced cuckoo search optimization with opposition-based learning for the optimal placement of sensor nodes and enhanced network coverage in wireless sensor networks,” Appl. Sci., vol. 15, no. 15, Art. no. 8575, Aug. 2025, doi: 10.3390/app15158575.
T. K. Samal, S. C. Patra, and M. R. Kabat, “An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1845–1856, 2022. doi: 10.1016/j.jksuci.2019.11.002.
J. Jeyaboopathiraja, P. Mariajohn, S. S. Maidin, and J. Sun, “An adaptive cuckoo search algorithm with deep learning for addressing cybersecurity problems,” vol. 5, no. 4, pp. 1977–1988, 2024
Y. Yang, X. Zuo, M. Fu, S. Yu, and C. Jia, “cuckoo search algorithm based on dynamic adjustment mechanism,” vol. 32, no. 5, pp. 171–183, 2021, doi: 10.53106/199115992021103205014.
Y. Xiong, Z. Zou, and J. Cheng, “Cuckoo search algorithm based on cloud model and its application,” Sci. Rep., vol. 13, no. 1, pp. 1–13, 2023, doi: 10.1038/s41598-023-37326-3.
H. M. Abdulwahab, S. Ajitha, M. A. N. Saif, B. A. H. Murshed, and F. A. Ghanem, “MOBCSA: Multi-objective binary cuckoo search algorithm for feature selection in bioinformatics,” IEEE Access, vol. 12, pp. 21840–21867, 2024, doi: 10.1109/ACCESS.2024.3362228.
SS. H. Anwariningsih, Wahyono, and R. Sumiharto, “An improved cuckoo search algorithm with dynamic parameters and hybrid distribution for enhanced CLAHE,” Eng. Technol. Appl. Sci. Res., vol. 15, no. 4, pp. 24148–24158, Aug. 2025, doi: 10.48084/etasr.10652.
S. K. Meena, S. S. Singh, and K. Singh, “search optimization-based influence maximization in dynamic social networks,” ACM Trans. Web, vol. 18, no. 4, 2024, doi: 10.1145/3690644.
J. vom Brocke, A. Hevner, and A. Maedche, “Introduction to design science research,” in Design Science Research. Cases, J. vom Brocke, A. Hevner, and A. Maedche, Eds. Cham, Switzerland: Springer, 2020, pp. 1–13, doi: 10.1007/978-3-030-46781-4_1.
P. Lakshminarayana and T. V. Sureshkumar, “Automatic generation and optimization of test cases using hybrid cuckoo search and bee colony algorithm,” J. Intell. Syst., vol. 30, no. 1, pp. 59–72, 2021, doi: 10.1515/jisys-2019-0051.
K. Pradyot, D. Sharma, and K. P. Gouthami, “Favourable test sequence generation in state-based testing using bat algorithm,” International Journal of Computer Applications in Technology, 2015. doi: 10.1504/IJCAT.2015.070495.
B. Hoseini and S. Jalili, “Automatic test path generation from sequence diagram using genetic algorithm,” in Proc. 7th Int. Symp. Telecommun. (IST), 2014, pp. 106–111, doi: 10.1109/ISTEL.2014.7000678.
P. Gulia and R. S. Chillar, “A new approach to generate and optimize test cases for UML state diagram using genetic algorithm,” ACM SIGSOFT Softw. Eng. Notes, vol. 37, no. 3, 2012, doi: 10.1145/2180921.2180933.
Z. Shi, X. Zeng, T. Zhang, L. Han, and Y. Qian, “UML diagram-driven test scenarios generation based on the temporal graph grammar,” KSII Trans. Internet Inf. Syst., vol. 15, no. 7, pp. 2476–2495, Jul. 2021, doi: 10.3837/tiis.2021.07.010.
P. Mahali and A. A. Acharya, “Model based test case prioritization using UML activity diagram and evolutionary algorithm,” Int. J. Comput. Sci. Informatics, vol. 4, no. 2, pp. 76–81, 2014.
N. Khurana and R. S. Chillar, “Test case generation using UML models and Genetic Algorithm,” Procedia Computer Science, 2015
G. A. Ansari, “Use of firefly algorithm in optimization and prioritization of test paths generated from UML sequence diagram,” International Journal of Computer Applications, vol. 167, no. 4, pp. 24–30, 2017
N. Khurana, R. S. Chhillar, and U. Chhillar, “A novel technique for generation and optimization of test cases using use case, sequence, activity diagram and genetic algorithm,” J. Softw., vol. 11, no. 3, pp. 242–250, Mar. 2016, doi: 10.17706/jsw.11.3.242-250.
S. U. Ahmed, S. A. Sahare, and A. Ahmed, “Automatic test case generation using collaboration UML diagrams,” World J. Sci. Technol., vol. 2, 2012.
S. K. Swain, D. P. Mohapatra, and R. Mall, “case generation based on use case and sequence diagram,” J. Softw. Eng., pp. 21–52, 2010.

Details

Primary Language

English

Subjects

Software Testing, Verification and Validation

Journal Section

Research Article

Authors

James Maina Mburu ^*
0009-0007-5192-3832
Kenya

John Ndia
0000-0002-9223-8096
Kenya

Samson Munialo
0000-0002-8654-3377
Kenya

Early Pub Date

October 29, 2026

Publication Date

October 30, 2026

Submission Date

January 28, 2026

Acceptance Date

July 14, 2026

Published in Issue

Year 2026 Volume: 9 Number: 5

DOI

https://doi.org/10.35377/saucis...1873936

IZ

https://izlik.org/JA62JZ85GE

Cite

RIS / Bibtex

APA

Mburu, J. M., Ndia, J., & Munialo, S. (2026). An Extended Adaptive Cuckoo Search Technique for Test Case Optimization. Sakarya University Journal of Computer and Information Sciences, 9(5), 1667-1685. https://doi.org/10.35377/saucis...1873936

AMA

1.Mburu JM, Ndia J, Munialo S. An Extended Adaptive Cuckoo Search Technique for Test Case Optimization. SAUCIS. 2026;9(5):1667-1685. doi:10.35377/saucis.1873936

Chicago

Mburu, James Maina, John Ndia, and Samson Munialo. 2026. “An Extended Adaptive Cuckoo Search Technique for Test Case Optimization”. Sakarya University Journal of Computer and Information Sciences 9 (5): 1667-85. https://doi.org/10.35377/saucis. 1873936.

EndNote

Mburu JM, Ndia J, Munialo S (October 1, 2026) An Extended Adaptive Cuckoo Search Technique for Test Case Optimization. Sakarya University Journal of Computer and Information Sciences 9 5 1667–1685.

IEEE

[1]J. M. Mburu, J. Ndia, and S. Munialo, “An Extended Adaptive Cuckoo Search Technique for Test Case Optimization”, SAUCIS, vol. 9, no. 5, pp. 1667–1685, Oct. 2026, doi: 10.35377/saucis...1873936.

ISNAD

Mburu, James Maina - Ndia, John - Munialo, Samson. “An Extended Adaptive Cuckoo Search Technique for Test Case Optimization”. Sakarya University Journal of Computer and Information Sciences 9/5 (October 1, 2026): 1667-1685. https://doi.org/10.35377/saucis. 1873936.

JAMA

1.Mburu JM, Ndia J, Munialo S. An Extended Adaptive Cuckoo Search Technique for Test Case Optimization. SAUCIS. 2026;9:1667–1685.

MLA

Mburu, James Maina, et al. “An Extended Adaptive Cuckoo Search Technique for Test Case Optimization”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 5, Oct. 2026, pp. 1667-85, doi:10.35377/saucis. 1873936.

Vancouver

1.James Maina Mburu, John Ndia, Samson Munialo. An Extended Adaptive Cuckoo Search Technique for Test Case Optimization. SAUCIS. 2026 Oct. 1;9(5):1667-85. doi:10.35377/saucis. 1873936