摘 要
软件缺陷预测是提升软件质量与降低开发成本的重要手段，随着机器学习技术的快速发展，其在软件缺陷预测领域的应用日益广泛。本研究旨在探索基于机器学习的软件缺陷预测模型，以提高预测精度和实用性。研究背景源于传统缺陷预测方法在处理复杂数据时的局限性，以及对高维、非线性特征的有效建模需求。为此，本文提出了一种集成多种机器学习算法的预测框架，结合特征选择、数据平衡及模型优化等关键技术。具体而言，通过对比分析支持向量机、随机森林、梯度提升决策树等主流算法的性能，并引入深度学习模型以捕捉更深层次的特征关系。实验结果表明，所提出的模型在多个公开数据集上表现出显著优势，特别是在F1分数和AUC指标上超越了现有方法。此外，该模型具备较强的泛化能力，能够适应不同类型的软件项目。本研究的主要创新点在于提出了一个可扩展的预测框架，同时融合了传统机器学习与深度学习的优势，并针对软件缺陷数据的特点进行了针对性优化。这一成果为软件工程领域提供了新的研究思路，也为实际工程应用中的缺陷检测提供了有效工具。
关键词：软件缺陷预测；机器学习；深度学习；特征选择；模型优化
Abstract
Software defect prediction is a critical approach to enhancing software quality and reducing development costs. With the rapid advancement of machine learning technologies, their applications in software defect prediction have become increasingly widespread. This study aims to explore machine-learning-based software defect prediction models to improve prediction accuracy and practical applicability. The research context arises from the limitations of traditional defect prediction methods when handling complex data, as well as the need for effective modeling of high-dimensional and nonlinear features. To address these challenges, this paper proposes a predictive fr amework that integrates multiple machine learning algorithms, incorporating key techniques such as feature selection, data balancing, and model optimization. Specifically, the performance of mainstream algorithms, including support vector machines, random forests, and gradient boosting decision trees, is analyzed comparatively, while deep learning models are introduced to capture deeper feature relationships. Experimental results demonstrate that the proposed model exhibits significant advantages on several public datasets, particularly surpassing existing methods in terms of F1 score and AUC metrics. Moreover, the model demonstrates strong generalization capabilities, adapting effectively to various types of software projects. The primary innovation of this study lies in the proposal of an extensible predictive fr amework that combines the strengths of traditional machine learning and deep learning, with targeted optimizations tailored to the characteristics of software defect data. This achievement provides new research directions for the field of software engineering and offers effective tools for defect detection in practical engineering applications.
Keywords: Software Defect Prediction; Machine Learning; Deep Learning; Feature Selection; Model Optimization
目 录
1绪论 1
1.1软件缺陷预测的研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2机器学习在软件缺陷预测中的应用基础 2
2.1软件缺陷预测的基本概念 2
2.2常见机器学习算法介绍 3
2.3数据集构建与特征选择方法 3
2.4模型评估指标体系建立 4
2.5应用基础总结 4
3软件缺陷预测模型的设计与优化 5
3.1模型设计的核心问题分析 5
3.2特征工程对模型性能的影响 5
3.3不同算法的对比实验设计 6
3.4模型参数调优策略研究 6
3.5高效模型设计方案 7
4实验验证与结果分析 7
4.1实验环境与数据准备 7
4.2预测模型的实际测试过程 8
4.3结果对比与性能评估 8
4.4错误分析与改进方向探讨 9
4.5实验结论总结 9
结论 10
参考文献 11
致 谢 12
