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计算机工程 ›› 2009, Vol. 35 ›› Issue (15): 201-202,. doi: 10.3969/j.issn.1000-3428.2009.15.070

• 人工智能及识别技术 • 上一篇    下一篇

用于测试用例最小化问题的改进PSO算法

孙家泽,王曙燕,曹小鹏   

  1. (西安邮电学院计算机科学与技术系,西安 710061)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-08-05 发布日期:2009-08-05

Improved PSO Algorithm for Test Suite Minimization Problem

SUN Jia-ze, WANG Shu-yan, CAO Xiao-peng   

  1. (Department of Computer Science and Technology, Xi’an Institute of Posts and Telecommunications, Xi’an 710061)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-08-05 Published:2009-08-05

摘要: 针对回归测试中测试用例最小化问题,将粒子群优化算法和随机算法相结合,提出一种二维随机粒子群优化算法,用来解决测试用例最小化的问题。该算法采用二维适应值评价函数,一维是覆盖度,另一维是冗余度。利用各个测试用例的覆盖率为概率随机产生下一代个体位置。实验结果表明该算法性能优良且具有较好的稳定性。

关键词: 回归测试, 测试用例最小化, 粒子群优化算法, 随机算法

Abstract: A hybrid algorithm is proposed by combining Particle Swarm Optimization(PSO) algorithm with a stochastic optimization method, for solving the test suite minimization problem in regression test. In the algorithm, fitness function is double dimensions, which are coverage and redundancy, and the position of the particle is produced by stochastic algorithm, which is randomly generated by the probability of test coverage of test suite. Simulation results show that the algorithm is effective and has better stabilization.

Key words: regression test, test suite minimization, Particle Swarm Optimization(PSO) algorithm, stochastic algorithm

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