蹇紅梅， 成新文， 曾  燕
（四川理工學(xué)院計(jì)算機(jī)學(xué)院，四川 自貢 643000）
摘  要：針對(duì)軟件測(cè)試數(shù)據(jù)采用遺傳算法和粒子群算法自動(dòng)生成算法復(fù)雜和容易早熟等問題，提出一種動(dòng)態(tài)調(diào)整收縮擴(kuò)張因子的自適應(yīng)量子粒子群算法（AQPSO）。該算法通過(guò)引入粒子進(jìn)化度和聚合度，收縮擴(kuò)張因子隨粒子進(jìn)化度因子和聚合度因子變化而變化，從而實(shí)現(xiàn)算法的動(dòng)態(tài)自適應(yīng)性，提高算法收斂速度和求解精度。軟件測(cè)試數(shù)據(jù)自動(dòng)生成實(shí)驗(yàn)驗(yàn)證了該算法的有效性和可行性。
關(guān)鍵詞：量子粒子群；軟件測(cè)試；測(cè)試數(shù)據(jù)生成；收縮擴(kuò)張因子
中圖分類號(hào)：TP206+.1；TP301.6；TP311.52；TP311.55     文獻(xiàn)標(biāo)志碼：A    文章編號(hào)：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