Test case generation method based on particle swarm optimization algorithm

Abstract

To address the problem of low efficiency in test case generation, an Elite Opposition-Learning Particle Swarm Optimization Based on Selection and Mutation Strategy (SM-EOLPSO) is proposed in this paper. Firstly, nonlinear decreasing inertia weight with random offset is set so that the search ability can be adaptively adjusted to the situation. Secondly, opposition-based learning is performed to enhance global detection ability and improve population diversity; meanwhile, selection and mutation operations in genetic algorithm are introduced to speed up convergence and prevent falling into local optimal solutions. Finally, the branch distance is used to construct the fitness function and evaluate test cases. Experimental results show that the algorithm is competitive in terms of the number of iterations and generation time for automatic test case generation

Combinatorial Test Case Generation Based on ROBDD and Improved Particle Swarm Optimization Algorithm

“…The proposed approach seeks to enhance the effectiveness of the search process and promote the generation of diverse and comprehensive test cases. To tackle the issue of low efficiency in test case generation, Wang et al [23] introduce a novel approach called Selection and Mutation Strategy-Based Elite Opposition-Learning Particle Swarm Optimization (SM-EOLPSO). Experimental results demonstrate that the proposed algorithm exhibits competitiveness in terms of the iteration count and generation time for automatic test case generation.…”

A Model and Exact Algorithm for Minimum Set Covering Problem

“…Chen Xiangyang et al proposed a hybrid algorithm of intensive reduction algorithm and improved genetic algorithm by simplifying the network and improving chromosome parameters to solve the set covering problem [20] . By improving the premature convergence and local extreme value of standard particle swarm optimization algorithm, Zhang Na et al put forward a particle swarm optimization algorithm based on reverse-learning and multi-search for test cases generation [21] . Jovanovic et al proposed an ant colony optimization algorithm by improving pheromone correction rules to prevent local extremum [22] .…”