吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

谱聚类与混沌免疫相结合的软件缺陷分析算法

徐毅1, 范会敏2   

  1. 1. 西北政法大学 商学院, 西安 710100; 2. 西安工业大学 计算机科学与工程学院, 西安 710021
  • 收稿日期:2016-03-16 出版日期:2016-09-26 发布日期:2016-09-19
  • 通讯作者: 范会敏 E-mail:fhm695@qq.com

Software Defect Analysis Algorithm Based on Combinationof Spectral Clustering and Chaotic Immunity

XU Yi1, FAN Huimin2   

  1. 1. School of Business, Northwest University of Political Science and Law, Xi’an 710100, China;2. School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, China
  • Received:2016-03-16 Online:2016-09-26 Published:2016-09-19
  • Contact: FAN Huimin E-mail:fhm695@qq.com

摘要:

针对谱聚类算法中K-means处理无标识软件度量元数据易陷入局部最优的问题, 提出一种新的混沌免疫聚类算法. 该方法在免疫克隆选择算法的框架下, 设计抗体亲和度计算方法用于免疫克隆聚类中心的评价, 并给出分层混沌变异算子, 进一步提高了无标识软件度量元数据的预测性能. 仿真实验验证了算法的有效性.

关键词: 软件缺陷预测, 软件度量元, 混沌理论, 谱聚类

Abstract:

Aiming at the problem that non marking software metric metadata was easy to fall into the local optimum of the K-means process in the spectral clustering algorithm, we proposed a new algorithm of chaotic immune clustering algorithm. Under the framework of the immune clonal selection algorithm, the method of antibody affinity calculation was designed for the evaluation of immune clonal clustering center, and the hierarchical chaotic mutation operator was given, which further improved  prediction performance of non marking software metric metadata. Simulation results show that the algorithm is effective.

Key words: software defect prediction, software metric, chaos theory, spectral clustering

中图分类号: 

  • TP311