摘要
针对复杂软件数据提取过程中特征权重难以固定,导致软件缺陷数据提取时间较长,且提取准确度偏低的问题,提出基于多特征权重分配的软件缺陷数据自适应提取方法.构建软件缺陷数据自适应提取模型,得到K均值数据聚类结果,将多特征权重特征量输入到BP神经网络分类器,通过时频分析法完成对多特征权重特征的分配;构建缺陷数据检测模型,完成软件缺陷数据的自适应提取.结果表明,该方法对软件缺陷数据自适应提取准确性更高,且提取时间较短,具有较高的提取效率.
Aiming at the problem that the feature weights are difficult to be fixed in the process of complex software data extraction,resulting in longer software defect data extraction time and lower extraction accuracy,an adaptive software defect data extraction method based on multiple-feature weight assignment was proposed.An adaptive extraction model for software defect data was established,K-means data clustering results were obtained,and multi-feature weight features were input into a BP neural network classifier.The multi-feature weight features were allocated by a time-frequency analysis method,a defect data detection model was constructed as well,and the adaptive extraction of software defect data was completed.The results show that the as-proposed method has higher accuracy,shorter extraction time and higher extraction efficiency in the adaptive extraction of software defect data.
作者
白凤凤
BAI Feng-feng(Department of Computer Science and Technology,Lüliang University,Lüliang 033000,China)
出处
《沈阳工业大学学报》
CAS
北大核心
2022年第6期677-681,共5页
Journal of Shenyang University of Technology
基金
国家大学生创新项目(2018637)
山西省教改项目(J2018193,J2019206).
关键词
复杂软件
缺陷数据
多特征权重
权重分配
自适应提取
K均值聚类
BP神经网络
时频分析
complex software
defect data
multi-feature weight
weight assignment
adaptive extraction
K-means clustering
BP neural network
time-frequency analysis
