A Hybrid Harmony Search and Particle Swarm Optimization Algorithm (HSPSO) for Testing Non-functional Properties in Software System
Nurudeen Muhammad Bala, Suhailan Safei · Statistics Optimization & Information Computing · 2021
An important aspect of improving software system is testing. However, it is time demanding and sometimeslabour intensive if done manually. In this paper, we developed an automatic search-based approach for testing the nonfunctional properties of a software system using hybrid harmony search and particle swarm optimization algorithms. The approach birthed a new algorithm named HSPSO, which is proposed based on the strength of HS over Genetic algorithm (GA) in terms of less adjustable parameters, quick convergence and smooth implementation. On the other hand, we propose the PSO to complement the drawback of HS in terms of time consumption problem. Besides, we used four programs for the comparative efficiency analysis of the proposed algorithm in relation to competing algorithms based on average branch coverage and execution time. The results from the analysis showed that the HSPSO algorithm was able to achieve 100% average coverage with a fewer number of generated test cases and under limited execution time.