一种基于粒子群优化的成对组合测试算法框架

摘要

This paper proposes a framework of particle swarm optimization (PSO) based pairwise testing. To systematically build pairwise test suites, two different PSO based strategies are proposed. One strategy takes on a one-test-at-a-time approach and the other takes on an IPO-like approach. In these two different strategies, PSO is used to complete the construction of a single test and research on how to formulate the search space, define the fitness function, and set some heuristic settings. To verify the effectiveness of this approach, these algorithms are implemented and some typical instances have been chosen. In this empirical study, the paper analyzes the impact factors of this framework and compares this approach to other well-known approaches in test suite size and generation time. Final empirical results show the competitiveness of this approach.%提出一种基于粒子群优化的成对组合测试用例集生成算法框架.在生成测试用例时,该框架采用粒子群优化尝试生成强组合覆盖能力的测试用例,并研究了搜索空间、适应值函数和启发式的合理设定；在构造组合测试用例集时,以上述测试用例生成算法为基础,提出两种策略:一种基于one-test-at-a-time,另一种基于类IPO.编程实现该算法框架,并通过实证研究分析了算法框架中不同设定对组合测试用例集规模的影响；最后,与现有的经典方法在组合测试用例集生成规模和算法执行时间上进行了比较,最终结果表明,该算法具有竞争力.