Automated test case generation is a crucial activity for ensuring the reliability, robustness, and quality of large-scale software systems. Conventional test generation techniques often face significant challenges in handling scalability, achieving high code coverage, and adapting to complex and heterogeneous software architectures. Evolutionary algorithms have emerged as effective solutions, capable of exploring large search spaces and generating test suites with improved coverage and fault detection capabilities. However, individual evolutionary strategies, such as genetic algorithms or particle swarm optimization, can exhibit premature convergence, limited exploration, or reduced efficiency when applied to large-scale and modular software systems. To address these limitations, this paper proposes a hybrid evolutionary algorithm (HEA) that combines the strengths of multiple evolutionary strategies, including genetic algorithms, particle swarm optimization, and local search techniques, to enhance both exploration and exploitation during automated test case generation. The HEA framework systematically evolves candidate test cases, optimizes coverage and fault detection, and adapts dynamically to the complexity of the software under test. Experimental results demonstrate that the proposed hybrid approach significantly improves code coverage, fault detection effectiveness, and execution efficiency compared to traditional evolutionary methods. This framework provides a scalable, robust, and adaptable solution for automated test case generation in modern large-scale software development environments.

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1. McMinn P. Search‐based software test data generation: A survey. Softw Test Verif Reliab. 2004 Jun;14(2):105–56.
2. Harman M, Jones BF. Search-based software engineering. Inf Softw Technol. 2001 Dec 15;43(14):833–9.
3. Panichella A, Kifetew FM, Tonella P. Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Trans Softw Eng. 2017 Feb 2;44(2):122–58.
4. Afzal W, Torkar R, Feldt R. A systematic review of search-based testing for non-functional system properties. Inf Softw Technol. 2009 Jun 1;51(6):957–76.
5. Hamlet D, Taylor R. Partition testing does not inspire confidence (program testing). IEEE Trans Softw Eng. 2002 Aug 6;16(12):1402–11.
6. Utting M, Legeard B. Practical model-based testing: a tools approach. Elsevier; 2010 Jul 27.
7. Ali S, Briand LC, Hemmati H, Panesar-Walawege RK. A systematic review of the application and empirical investigation of search-based test case generation. IEEE Trans Softw Eng. 2009 Aug 21;36(6):742–62.
8. Hekmatnejad M, Hoxha B, Fainekos G. Search-based test-case generation by monitoring responsibility safety rules. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC); 2020 Sep 20; pp. 1–8. IEEE.
9. Akbar MA, Khan AA, Hamza M, Ghaffar A, Hajikhani A. Agentic AI in Software Engineering: Practitioner Perspectives Across the Software Development Life Cycle. Software Engineering: Practitioner Perspectives Across the Software Development Life Cycle. 2025 Sep 16.
10. Yoo S, Harman M. Regression testing minimization, selection and prioritization: A survey. Softw Test Verif Reliab. 2012 Mar;22(2):67–120.

Automated test case generation is a crucial activity for ensuring the reliability, robustness, and quality of large-scale software systems. Conventional test generation techniques often face significant challenges in handling scalability, achieving high code coverage, and adapting to complex and heterogeneous software architectures. Evolutionary algorithms have emerged as effective solutions, capable of exploring large search spaces and generating test suites with improved coverage and fault detection capabilities. However, individual evolutionary strategies, such as genetic algorithms or particle swarm optimization, can exhibit premature convergence, limited exploration, or reduced efficiency when applied to large-scale and modular software systems. To address these limitations, this paper proposes a hybrid evolutionary algorithm (HEA) that combines the strengths of multiple evolutionary strategies, including genetic algorithms, particle swarm optimization, and local search techniques, to enhance both exploration and exploitation during automated test case generation. The HEA framework systematically evolves candidate test cases, optimizes coverage and fault detection, and adapts dynamically to the complexity of the software under test. Experimental results demonstrate that the proposed hybrid approach significantly improves code coverage, fault detection effectiveness, and execution efficiency compared to traditional evolutionary methods. This framework provides a scalable, robust, and adaptable solution for automated test case generation in modern large-scale software development environments.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. McMinn P. Search‐based software test data generation: A survey. Softw Test Verif Reliab. 2004 Jun;14(2):105–56.
2. Harman M, Jones BF. Search-based software engineering. Inf Softw Technol. 2001 Dec 15;43(14):833–9.
3. Panichella A, Kifetew FM, Tonella P. Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Trans Softw Eng. 2017 Feb 2;44(2):122–58.
4. Afzal W, Torkar R, Feldt R. A systematic review of search-based testing for non-functional system properties. Inf Softw Technol. 2009 Jun 1;51(6):957–76.
5. Hamlet D, Taylor R. Partition testing does not inspire confidence (program testing). IEEE Trans Softw Eng. 2002 Aug 6;16(12):1402–11.
6. Utting M, Legeard B. Practical model-based testing: a tools approach. Elsevier; 2010 Jul 27.
7. Ali S, Briand LC, Hemmati H, Panesar-Walawege RK. A systematic review of the application and empirical investigation of search-based test case generation. IEEE Trans Softw Eng. 2009 Aug 21;36(6):742–62.
8. Hekmatnejad M, Hoxha B, Fainekos G. Search-based test-case generation by monitoring responsibility safety rules. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC); 2020 Sep 20; pp. 1–8. IEEE.
9. Akbar MA, Khan AA, Hamza M, Ghaffar A, Hajikhani A. Agentic AI in Software Engineering: Practitioner Perspectives Across the Software Development Life Cycle. Software Engineering: Practitioner Perspectives Across the Software Development Life Cycle. 2025 Sep 16.
10. Yoo S, Harman M. Regression testing minimization, selection and prioritization: A survey. Softw Test Verif Reliab. 2012 Mar;22(2):67–120.