...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computers >Software Defect Prediction Using Non-Negative Matrix Factorization
【24h】

Software Defect Prediction Using Non-Negative Matrix Factorization

机译：使用非负矩阵分解的软件缺陷预测

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Quality is considered as an important issue in the fields of software engineering. However, building quality software is very expensive, in order to raise the effectiveness and efficiency of quality assurance and testing, software defect prediction is used to identify defect-prone modules in an upcoming version of a software system and help to allow the effort on those modules. Although many models have been proposed, this problem has not resolved thoroughly. For overcoming these limits, recent results show that researcher should pay more attention to improve the quality of the data. Aimed at this purpose, in this paper, we propose a novel approach to resolve the problem of software defect prediction. The method is classification using Non-Negative Matrix Factorization (NMF). In this paper, NMF algorithm is not only used for extracting external features but also as a powerful way for classification of software defect data. Experiments demonstrating the efficiency of the proposed approach are performed for software defect data classification. And the results show that it outperforms the state of the art techniques tested for this experiment. Finally, we suggest that it can be a useful and practical way addition to the framework of software quality prediction.
机译：质量被认为是软件工程领域的重要问题。但是，构建高质量的软件非常昂贵，为了提高质量保证和测试的有效性和效率，软件缺陷预测用于识别即将发布的软件系统版本中易于出现缺陷的模块，并帮助他们做出努力。模块。尽管已经提出了许多模型，但是这个问题尚未彻底解决。为了克服这些限制，最近的结果表明，研究人员应该更加注意提高数据的质量。为此，本文提出了一种解决软件缺陷预测问题的新方法。该方法是使用非负矩阵分解（NMF）进行分类的方法。本文将NMF算法不仅用于提取外部特征，而且作为对软件缺陷数据进行分类的有力方法。对软件缺陷数据分类进行了实验，证明了所提方法的有效性。结果表明，其性能优于本实验所测试的最新技术。最后，我们建议这可能是对软件质量预测框架进行补充的有用且实用的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号