Software Testing Case Generation of Ant Colony Optimization based on Quantum Dynamic Self-Adaptation

Abstract

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.