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提高软件缺陷预测:一个具有改进功能选择和整体机器学习的框架.

Misbah Ali1, Tehseen Mazhar1, Amal Al-Rasheed2

  • 1Department of Computer Science & Information Technology, Virtual University of Pakistan, Lahore, Pakistan.

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PubMed
概括
此摘要是机器生成的。

这项研究引入了软件缺陷预测的五阶段框架,将准确度提高到95.1%. 这种新的方法通过显著减少执行时间以更好地确保软件质量来提高效率.

关键词:
在这里,我们可以看到AIAIAI.深度学习是一种深度学习.合唱团组合在一起.功能选择 功能选择机器学习是机器学习.在这里,我们可以看到NB NB NB NB NB.质量质量质量质量质量质量质量质量.这是一个RN RN RN.在SVM中,SVM是SVM.软件缺陷预测软件缺陷预测

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科学领域:

  • 软件工程 软件工程 软件工程
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 有效的软件缺陷预测对于质量保证至关重要.
  • 目前的方法在准确性和效率方面面临挑战.
  • 早期发现有缺陷的模块可以改善软件开发生命周期.

研究的目的:

  • 为软件缺陷预测提出一个全面的五阶段框架.
  • 提高识别有缺陷软件模块的准确性和效率.
  • 解决软件缺陷预测领域的现有挑战.

主要方法:

  • 使用了清理过的NASA缺陷数据集 (CM1,JM1,MC2,MW1,PC1,PC3,PC4).
  • 应用遗传算法用于最佳特征选择.
  • 采用集体机器学习,随机森林,支持矢量机器和纯粹的贝叶斯作为基础分类器,与投票主分类器相结合.

主要成果:

  • 在软件缺陷预测中达到95.1%的最大准确度.
  • 与最先进的组合和基础分类器相比,表现出卓越的性能.
  • 显著减少了培训和测试执行时间,平均分别减少了51.52%和52.31%.

结论:

  • 拟议的五阶段框架对于准确的软件缺陷预测非常有效.
  • 该框架提供了一个计算经济的解决方案,提高了效率.
  • 该研究为软件质量保证提供了一个强大而优化的方法.