Automatic Path Test Data Generation Based on GA-PSO

摘要：

Automatic test data generation is a key issue to achieve test automation. The path test data generation is a hot point in the research Held of software test investigation. The previous approaches of generating test data are mostly based on Genetic Algorithms (GA) and its improved algorithm. These approaches have tow shortcomings: one is too complex to use and difficult to set parameters. The other is weak local search and slow convergence. We propose a hybrid algorithm (GA-PSO) which combines Genetic Algorithm and Particle Swarm Optimization (PSO) in this paper. The new algorithm is proved effective by a representative test of the triangle type of discrimination. The experiment shows that the new algorithm has higher performance when the value of φ is 20％.

关键词： Particle Swarm Optimization Genetic Algorithm test data generation GA-PSO Algorithm

作者: Sheng Zhang Ying Zhang Hong Zhou Qingquan He

作者单位: School of Information Engineering Nanchang Hongkong University Nanchang, Jiangxi Province 330063, Ch School of Information Engineering Nanchang Hongkong University Nanchang,Jiangxi Province 330063, Chi

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 142-146

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）