基于机器学习的软件缺陷预测模型构建与优化
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。本研究旨在构建并优化基于机器学习的软件缺陷预测模型，以期为软件工程领域提供更高效准确的缺陷检测方法。研究选取了多种经典机器学习算法，包括支持向量机、随机森林等，并引入深度学习中的卷积神经网络进行对比实验。针对传统特征提取方式存在局限性的问题，提出了一种融合代码静态分析与开发者行为数据的多源特征提取方法，有效提升了特征表达能力。同时，为解决数据集不平衡问题，采用SMOTE过采样技术结合ENN欠采样策略优化样本分布。通过在多个公开数据集上的实验验证，所构建模型在F1 - score、AUC等评价指标上均取得显著提升，相较于传统方法平均性能提升约15%。
关键词：软件缺陷预测 机器学习 多源特征提取
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 aims to construct and optimize software defect prediction models based on machine learning in order to provide more efficient and accurate defect detection methods for the software engineering field. A variety of classical machine learning algorithms, including support vector machine and random forest, and introduce convolutional neural network in deep learning for comparison experiments. In view of the limitations of traditional feature extraction methods, a multi-source feature extraction method that combines static analysis of code and developer behavior data is proposed, which effectively improves the feature ex pression ability. Meanwhile, to solve the problem of data set imbalance, the SMOTE oversampling technique combined with the ENN undersampling strategy was used to optimize the sample distribution. Through experimental verification on several publicly available data sets, the constructed model has achieved significant improvement in F1-score and AUC, with an average performance improvement of about 15% compared with the traditional methods.
Keyword:Software Defect Prediction Machine Learning Multi-source Feature Extraction
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 2
2机器学习算法选择与评估 2
2.1常用机器学习算法综述 2
2.2算法适用性分析 3
2.3模型性能评估指标 3
3数据预处理与特征工程 4
3.1数据收集与清洗 4
3.2特征选择与提取 5
3.3数据集划分策略 5
4模型构建与优化实践 6
4.1初始模型构建 6
4.2参数调优方法 7
4.3模型集成技术 7
结论 8
参考文献 9
致谢 10
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。本研究旨在构建并优化基于机器学习的软件缺陷预测模型，以期为软件工程领域提供更高效准确的缺陷检测方法。研究选取了多种经典机器学习算法，包括支持向量机、随机森林等，并引入深度学习中的卷积神经网络进行对比实验。针对传统特征提取方式存在局限性的问题，提出了一种融合代码静态分析与开发者行为数据的多源特征提取方法，有效提升了特征表达能力。同时，为解决数据集不平衡问题，采用SMOTE过采样技术结合ENN欠采样策略优化样本分布。通过在多个公开数据集上的实验验证，所构建模型在F1 - score、AUC等评价指标上均取得显著提升，相较于传统方法平均性能提升约15%。
关键词：软件缺陷预测 机器学习 多源特征提取
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 aims to construct and optimize software defect prediction models based on machine learning in order to provide more efficient and accurate defect detection methods for the software engineering field. A variety of classical machine learning algorithms, including support vector machine and random forest, and introduce convolutional neural network in deep learning for comparison experiments. In view of the limitations of traditional feature extraction methods, a multi-source feature extraction method that combines static analysis of code and developer behavior data is proposed, which effectively improves the feature ex pression ability. Meanwhile, to solve the problem of data set imbalance, the SMOTE oversampling technique combined with the ENN undersampling strategy was used to optimize the sample distribution. Through experimental verification on several publicly available data sets, the constructed model has achieved significant improvement in F1-score and AUC, with an average performance improvement of about 15% compared with the traditional methods.
Keyword:Software Defect Prediction Machine Learning Multi-source Feature Extraction
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 2
2机器学习算法选择与评估 2
2.1常用机器学习算法综述 2
2.2算法适用性分析 3
2.3模型性能评估指标 3
3数据预处理与特征工程 4
3.1数据收集与清洗 4
3.2特征选择与提取 5
3.3数据集划分策略 5
4模型构建与优化实践 6
4.1初始模型构建 6
4.2参数调优方法 7
4.3模型集成技术 7
结论 8
参考文献 9
致谢 10
