基于改进粒子群算法的变异体选择优化

发布时间：2018-04-09 23:12

本文选题：软件测试　切入点：变异测试　出处：《计算机应用研究》2017年03期

【摘要】：变异测试是常用的测试方法之一,变异测试分析的过程中计算开销会比较大,问题主要集中于测试过程中会产生大量的变异体。为了减少变异体的数量,提出用标准粒子群聚类算法进行选择优化,但标准粒子群算法在被测数据量增加到一定数量的时候,它的迭代次数就会增加、收敛速度就会下降。针对以上问题提出基于改进的粒子群算法对变异体进行选择优化。通过对变异体集合进行聚类分区,增强变异体集合的多态性,从而对粒子群算法进行改进优化。实验结果表明,在不影响测试充分度的前提下,使变异体的数量大幅度减少,同时与K-means算法以及标准粒子群算法相比之下,改进后的方法具有更好的优化效果。
[Abstract]:Mutation testing is one of the commonly used testing methods. The computational cost of mutation testing and analysis is relatively large. The problem is mainly focused on the process of testing will produce a large number of variants.In order to reduce the number of variants, the standard particle swarm clustering algorithm is proposed for selection optimization. However, when the amount of measured data is increased to a certain amount, the iteration times will increase and the convergence rate will decrease.To solve the above problems, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the selection of variants.By clustering and partitioning the variant set, the polymorphism of the variant set is enhanced, and the particle swarm optimization algorithm is improved.The experimental results show that, without affecting the test adequacy, the number of variants is greatly reduced, and the improved method has better optimization effect than the K-means algorithm and the standard particle swarm optimization algorithm.
【作者单位】：西安邮电大学计算机学院;
【基金】：陕西省自然科学基金资助项目(2015JM6359) 西安市科技计划资助项目(CXY1516(4)) 2016年陕西省工业攻关资助项目(2016GY-089)
【分类号】：TP311.53;TP18

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