期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2015
卷号：8
期号：9
页码：95-104
DOI：10.14257/ijhit.2015.8.9.11
出版社：SERSC
摘要：Automatic generation of testing data is the most crucial technology in the testing phase with some certain real value to improve software's testing automation degree. Although some certain results have been achieved by introducing the ant colony optimization to the testing process, the algorithm itself has the defects like too fast convergence speed and being easy to fall into local optimum. Based on this, self-adaptive factor is introduced in this paper to balance the algorithm's local optimum. Meanwhile, quantum algorithm is introduced to realize the individual renewal of the ant colony and reduce the ant colony's size so as to further optimize the target problem. Simulation experiments show that in the multipath test, algorithm in this paper has achieved remarkable progress compared with the ant colony optimization and accuracy of automatic data's optimal solution has been improved
关键词：Quantum Algorithm; Self-Adaptive Factor; Ant Colony Optimization
