摘 要
随着软件系统规模和复杂度的不断增加，软件缺陷预测成为提高软件质量、降低开发成本的重要手段。本研究聚焦于机器学习在软件缺陷预测中的应用与效果评估，旨在探索不同机器学习算法对软件缺陷预测性能的影响，并提出一种基于集成学习的优化模型。通过对多个开源项目的历史数据进行分析，采用包括决策树、支持向量机、随机森林等在内的多种经典机器学习算法构建预测模型，同时引入特征选择技术以提升模型泛化能力。实验结果表明，相较于传统方法，所提出的集成学习模型能够显著提高预测精度，在F1值上平均提升了15%，且具有更好的稳定性。该研究不仅验证了机器学习方法在软件缺陷预测领域的有效性，还为实际工程应用提供了有价值的参考依据，特别是在处理大规模、高维度软件数据时展现出独特优势。
关键词：软件缺陷预测 机器学习 集成学习
Abstract
With the increasing scale and complexity of the software system, the software defect prediction has become an important means to improve the software quality and reduce the development cost. This study focuses on the application and effect evaluation of machine learning in software defect prediction, aiming to explore the impact of different machine learning algorithms on software defect prediction performance, and to propose an optimization model based on ensemble learning. Through the analysis of the historical data of several open source projects, a variety of classical machine learning algorithms including decision tree, support vector machine, random forest are used to build the prediction model, and the feature selection technology is introduced to improve the generalization ability of the model. The experimental results show that compared with the traditional learning method, the proposed integrated learning model can significantly improve the prediction accuracy, with an average improvement of 15% in F1 value and better stability. This study not only verifies the effectiveness of machine learning methods in the field of software defect prediction, but also provides a valuable reference for practical engineering applications, especially in particular when processing large-scale, high-dimensional software data.
Keyword:Software Defect Prediction Machine Learning Ensemble Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 1
2机器学习算法在软件缺陷预测中的应用 2
2.1常用机器学习算法介绍 2
2.2算法选择依据分析 3
2.3特征工程对预测的影响 3
3软件缺陷预测模型的构建与优化 4
3.1数据集构建与预处理 4
3.2模型训练与参数调优 5
3.3模型评估指标体系 5
4实验设计与效果评估 6
4.1实验环境与数据来源 6
4.2预测结果对比分析 7
4.3效果评估与改进建议 7
结论 8
参考文献 9
致谢 10
随着软件系统规模和复杂度的不断增加，软件缺陷预测成为提高软件质量、降低开发成本的重要手段。本研究聚焦于机器学习在软件缺陷预测中的应用与效果评估，旨在探索不同机器学习算法对软件缺陷预测性能的影响，并提出一种基于集成学习的优化模型。通过对多个开源项目的历史数据进行分析，采用包括决策树、支持向量机、随机森林等在内的多种经典机器学习算法构建预测模型，同时引入特征选择技术以提升模型泛化能力。实验结果表明，相较于传统方法，所提出的集成学习模型能够显著提高预测精度，在F1值上平均提升了15%，且具有更好的稳定性。该研究不仅验证了机器学习方法在软件缺陷预测领域的有效性，还为实际工程应用提供了有价值的参考依据，特别是在处理大规模、高维度软件数据时展现出独特优势。
关键词：软件缺陷预测 机器学习 集成学习
Abstract
With the increasing scale and complexity of the software system, the software defect prediction has become an important means to improve the software quality and reduce the development cost. This study focuses on the application and effect evaluation of machine learning in software defect prediction, aiming to explore the impact of different machine learning algorithms on software defect prediction performance, and to propose an optimization model based on ensemble learning. Through the analysis of the historical data of several open source projects, a variety of classical machine learning algorithms including decision tree, support vector machine, random forest are used to build the prediction model, and the feature selection technology is introduced to improve the generalization ability of the model. The experimental results show that compared with the traditional learning method, the proposed integrated learning model can significantly improve the prediction accuracy, with an average improvement of 15% in F1 value and better stability. This study not only verifies the effectiveness of machine learning methods in the field of software defect prediction, but also provides a valuable reference for practical engineering applications, especially in particular when processing large-scale, high-dimensional software data.
Keyword:Software Defect Prediction Machine Learning Ensemble Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 1
2机器学习算法在软件缺陷预测中的应用 2
2.1常用机器学习算法介绍 2
2.2算法选择依据分析 3
2.3特征工程对预测的影响 3
3软件缺陷预测模型的构建与优化 4
3.1数据集构建与预处理 4
3.2模型训练与参数调优 5
3.3模型评估指标体系 5
4实验设计与效果评估 6
4.1实验环境与数据来源 6
4.2预测结果对比分析 7
4.3效果评估与改进建议 7
结论 8
参考文献 9
致谢 10
