Authors : Nurudeen Muhammad Bala; Sadiq Umar Abubakar; Buhari Mohammed Abubakar; Musabi Mohammed Shitta; Umar Ahmadu Ardo; Okoro Edith; Abbas Salamatu; Abdulrasheed Badamasuiy; Hauwa Dauda Balami; Bashar Muhammad Arzika

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/2vx7dcha

Scribd : https://tinyurl.com/ytfryj79

DOI : https://doi.org/10.38124/ijisrt/26feb433

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Software testing ensures quality and reliability of software necessitates effective testing; however, traditional manual testing methods can be time-consuming and inefficient. Automated test case generation (ATCG) has emerged as a promising solution, utilizing metaheuristic algorithms to enhance efficiency, minimize human effort, and improve fault detection. This paper presents an in-depth review of metaheuristic techniques employed in ATCG, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and other evolutionary and swarm-based methods. It also explores hybrid models that integrate multiple metaheuristic strategies or combine them with machine learning and symbolic execution to boost performance. The study addresses key challenges such as scalability, computational complexity, and constraint handling, while discussing optimization strategies to improve the effectiveness, diversity, and efficiency of generated test cases. Additionally, it examines hybrid approaches like the Hybrid Harmony Search and Particle Swarm Optimization (HSPSO) framework, analyzing their advantages, limitations, and potential enhancements. By outlining current trends, open research challenges, and future directions, this paper provides valuable insights for researchers and practitioners in automated software testing.

Keywords : Automated Test Case Generation (ATCG), Metaheuristic Algorithms, Hybrid Models, Software Testing, Optimization Techniques.

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