摘要
本文深入探讨了机器学习在软件缺陷预测领域的应用及其优化策略。首先介绍了软件缺陷的定义、分类以及软件缺陷预测的基本原理与流程，为后续研究奠定了理论基础。接着详细阐述了机器学习的基本原理与分类，并分析了机器学习在软件缺陷预测中的适用性，揭示了其在这一领域的巨大潜力。文章通过具体实例，展示了随机森林、支持向量机和深度学习等机器学习算法在软件缺陷预测中的实际应用效果。此外本文还重点探讨了如何优化机器学习在软件缺陷预测中的应用，包括特征选择与降维、模型融合与集成学习以及超参数调优与自动化机器学习等策略，旨在提高预测精度和效率。最后总结了本文的主要观点和研究成果，指出了未来研究方向。
关键词：软件缺陷预测；机器学习；应用优化；预测精度
Abstract
This paper discusses the application of machine learning in the field of software defect prediction and its optimization strategies. Firstly, the definition, classification and basic principles and processes of software defect prediction are introduced, which lays a theoretical foundation for subsequent research. Then, the basic principles and classification of machine learning are elaborated, and the applicability of machine learning in software defect prediction is analyzed, revealing its great potential in this field. This paper shows the practical application effect of machine learning algorithms such as random forest, support vector machine and deep learning in software defect prediction through specific examples. In addition, this paper focuses on how to optimize the application of machine learning in software defect prediction, including feature selection and dimensionality reduction, model fusion and ensemble learning, hyperparameter tuning and automatic machine learning, etc., to improve prediction accuracy and efficiency. Finally, the main views and research results of this paper are summarized, and the future research directions are pointed out.
Keywords：Software defect prediction; Machine learning; Application optimization; prediction accuracy
目 录
摘要 I
Abstract II
一、 绪论 1
（一）研究背景及意义 1
（二）研究目的 1
（三）国内外研究现状 1
二、相关理论基础 3
（一）软件缺陷的定义和分类 3
（二）软件缺陷预测的基本原理与流程 3
（三）机器学习的基本原理与分类 4
（四）机器学习在软件缺陷预测中的适用性分析 5
三、机器学习在软件缺陷预测中的具体应用实例 6
（一）基于随机森林的软件缺陷预测 6
（二）支持向量机在软件缺陷预测中的应用 6
（三）深度学习在软件缺陷预测中的实践 7
四、机器学习在软件缺陷预测中的应用优化 8
（一）特征选择与降维优化 8
（二）模型融合与集成学习优化 8
（三）超参数调优与自动化机器学习 9
结 论 10
参考文献 11
本文深入探讨了机器学习在软件缺陷预测领域的应用及其优化策略。首先介绍了软件缺陷的定义、分类以及软件缺陷预测的基本原理与流程，为后续研究奠定了理论基础。接着详细阐述了机器学习的基本原理与分类，并分析了机器学习在软件缺陷预测中的适用性，揭示了其在这一领域的巨大潜力。文章通过具体实例，展示了随机森林、支持向量机和深度学习等机器学习算法在软件缺陷预测中的实际应用效果。此外本文还重点探讨了如何优化机器学习在软件缺陷预测中的应用，包括特征选择与降维、模型融合与集成学习以及超参数调优与自动化机器学习等策略，旨在提高预测精度和效率。最后总结了本文的主要观点和研究成果，指出了未来研究方向。
关键词：软件缺陷预测；机器学习；应用优化；预测精度
Abstract
This paper discusses the application of machine learning in the field of software defect prediction and its optimization strategies. Firstly, the definition, classification and basic principles and processes of software defect prediction are introduced, which lays a theoretical foundation for subsequent research. Then, the basic principles and classification of machine learning are elaborated, and the applicability of machine learning in software defect prediction is analyzed, revealing its great potential in this field. This paper shows the practical application effect of machine learning algorithms such as random forest, support vector machine and deep learning in software defect prediction through specific examples. In addition, this paper focuses on how to optimize the application of machine learning in software defect prediction, including feature selection and dimensionality reduction, model fusion and ensemble learning, hyperparameter tuning and automatic machine learning, etc., to improve prediction accuracy and efficiency. Finally, the main views and research results of this paper are summarized, and the future research directions are pointed out.
Keywords：Software defect prediction; Machine learning; Application optimization; prediction accuracy
目 录
摘要 I
Abstract II
一、 绪论 1
（一）研究背景及意义 1
（二）研究目的 1
（三）国内外研究现状 1
二、相关理论基础 3
（一）软件缺陷的定义和分类 3
（二）软件缺陷预测的基本原理与流程 3
（三）机器学习的基本原理与分类 4
（四）机器学习在软件缺陷预测中的适用性分析 5
三、机器学习在软件缺陷预测中的具体应用实例 6
（一）基于随机森林的软件缺陷预测 6
（二）支持向量机在软件缺陷预测中的应用 6
（三）深度学习在软件缺陷预测中的实践 7
四、机器学习在软件缺陷预测中的应用优化 8
（一）特征选择与降维优化 8
（二）模型融合与集成学习优化 8
（三）超参数调优与自动化机器学习 9
结 论 10
参考文献 11
