蹇紅梅， 成新文， 曾  燕
（四川理工學院計算機學院，四川 自貢 643000）
摘  要：針對軟件測試數據采用遺傳算法和粒子群算法自動生成算法復雜和容易早熟等問題，提出一種動態調整收縮擴張因子的自適應量子粒子群算法（AQPSO）。該算法通過引入粒子進化度和聚合度，收縮擴張因子隨粒子進化度因子和聚合度因子變化而變化，從而實現算法的動態自適應性，提高算法收斂速度和求解精度。軟件測試數據自動生成實驗驗證了該算法的有效性和可行性。
關鍵詞：量子粒子群；軟件測試；測試數據生成；收縮擴張因子
中圖分類號：TP206+.1；TP301.6；TP311.52；TP311.55     文獻標志碼：A    文章編號：1674-5124（2013）03-0100-04
Automatic generation of software test data based on adaptive QPSO algorithm
JIAN Hong-mei， CHENG Xin-wen， ZENG Yan
（School of Computer Science，Sichuan University of Science & Engineering，Zigong 643000，China）
Abstract: For the complexity and prematurity of the automatic software test data generation algorithm based on the genetic algorithm and the standard particle swarm optimization algorithm， an adaptive quantum-behaved particle swarm optimization （AQPSO） algorithm is presented to dynamically adjust the contraction expansion factro to overcome these shortcomings. By introducing the evolution degree and polymerization degree of the particle into this method， the contraction expansion factor keeps changing as the evolution dgree and polymerization dgree factors are changing， orderly the dynamical and adaptive algorithm is realize， which improves the convergence speed and precision the traditional algorithm. The experiment on automatic generation of software test data verified the validity and feasibility of the algorithm.
Key words: QPSO； software testing； test data generation； contraction expansion factor