Efficient Path Coverage-based Test Data Generation using an Enhanced Pelican Algorithm

Abstract

Abstract White box test data generation typically relies on an optimized search through the program input space. Metaheuristic algorithms, such as Genetic Algorithms, Particle Swarm Optimization, and Simulated Annealing, are commonly utilized to address this problem. However, it is observed that existing algorithms often fall short in generating diverse test data. Their primary focus is identifying the optimal solution rather than a diverse set of reasonable solutions. This paper aims to address the issue of limited diversity in test data generation by proposing a modified version of the Pelican Optimization Algorithm (POA). The goal is to improve coverage and reduce the fitness evaluations required for generating test data. Additionally, the paper aims to tackle the challenge of minimizing test data volume while achieving high coverage, which is a significant concern in automatic test data generation. The proposed approach introduces the adapted POA to solve the diversity problem in test data generation. The modified algorithm outperforms eight well-known metaheuristic algorithms regarding coverage and the number of fitness evaluations needed. The approach also incorporates techniques to address the challenge of reducing test data volume while maintaining high coverage. Compared to similar well-known methods, our enhanced Pelican algorithm can improve test coverage by up to 83% when generating a thousand test data for benchmark programs. Without a doubt, the diversity in test data leads to less overlap between the paths covered by the test data, which in turn results in increased path coverage and improved test effectiveness. The superior performance of the adapted POA highlights its effectiveness in generating diverse and high-coverage test data.