关键词: 关键词：路径覆盖测试用例自动生成, 相似性特征, 多任务优化, 多因子回溯搜索优化算法

Abstract: Abstract:Abstract: Automated test case generation for path coverage (ATCG-PC) problem is a hot topic in the field of automated software testing. The fitness functions popularly used by swarm intelligence evolutionary algorithms in ATCG-PC problem are highly similar, but existing swarm intelligence evolutionary algorithms for solving ATCG-PC problem have not fully utilize the similarity feature. Inspired by the similarity feature, the two similar fitness functions were treated as two tasks, so that ATCG-PC problem was transformed into a multitasking ATCG-PC problem. A new swarm intelligent evolutionary algorithm called multifactorial backtracking search optimization algorithm (MFBSA) was proposed to solve multitasking ATCG-PC problem. The algorithm uses the memory population function of multifactorial selection I to improve its global search ability, and enables similar tasks to improve each other's optimization efficiency through knowledge transfer by assortative memory mating. The performance of MFBSA was evaluated on six fog computing test programs and six natural language processing test programs. Compared with backtracking search optimization algorithm (BSA), immune genetic algorithm (IGA), particle swarm optimization with convergence speed controller (PSO-CSC), adaptive particle swarm optimization algorithm (APSO) and differential evolution with hypercube-based learning strategies (DE- H), the test cases used by MFBSA covering the paths on 12 test programs reduced by 64.46%, 66.64%, 67.99%, 74.15% and 61.97%, respectively. The experimental results show that the proposed algorithm can effectively reduce testing cost.

Key words: Keywords: automated test case generation for path coverage, similarity feature, multitasking optimization, multifactorial backtracking search optimization algorithm