Comparative analysis of the Latest Improvements of Particle Swarm Optimization algorithms on automated software test data generation

Subject Areas : Information Technology in Engineering Design (ITED) Journal

Mojtaba salehi ¹ , Saeed Parsa ^{2 *} , saba Joudaki ³ , Hooshang Kolivand ⁴

1 - Department of Computer Engineering, Borujerd Branch, Islamic Azad University, Borujerd, Iran
2 -
3 - Department of Computer Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran
4 - School of computer science and mathematics, faculty of engineering and technology, liverpool john moores university, united kingdom

Received: 2024-11-28 Accepted : 2025-05-17 Published : 2025-08-02

Keywords: test data generation, meta-heuristic algorithms, Particle Swarm Optimization, path coverage,

Abstract :

This study explores improvements in Particle Swarm Optimization (PSO) algorithms for automated software test data generation, focusing on path coverage in software systems. PSO, valued for its scalability and simplicity, has been enhanced through various algorithmic improvements to address challenges like local optima entrapment and convergence inefficiencies. The research evaluates ten recent PSO variants on benchmark programs, comparing their performance based on coverage, runtime, and success rates. Among these, the A5 algorithm demonstrated superior performance, excelling in exploration-exploitation balance, population diversity, and convergence efficiency. Experimental results affirm the efficacy of meta-heuristic approaches in generating test data across expansive search spaces, positioning A5 as a leading solution for test data generation in optimizing software testing processes.

 

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