摘 要
软件缺陷预测是软件质量保证的重要组成部分，随着软件规模和复杂度的不断增加，传统的基于人工审查的方法已难以满足需求。为此，本研究旨在构建一种高效准确的软件缺陷预测模型，以辅助开发人员在早期阶段识别潜在缺陷模块。通过收集来自多个开源项目的代码库数据，采用静态代码分析技术提取特征变量，包括代码复杂度、变更频率等多维度指标，并引入深度学习算法进行建模。与传统机器学习方法相比，所提出的基于卷积神经网络的模型能够自动学习特征表示，无需人工设计特征工程，从而提高了模型的泛化能力和预测精度。实验结果表明，在多个评估指标上，该模型均显著优于现有方法，特别是在F1 - score方面表现突出，达到了0.85以上。此外，通过对模型内部结构的解释性分析，发现某些特定代码特征与缺陷发生存在强关联关系，为理解软件缺陷成因提供了新视角。本研究不仅为软件缺陷预测领域带来了创新性的解决方案，也为后续研究奠定了理论基础和技术支持。
关键词：软件缺陷预测 卷积神经网络 静态代码分析
Abstract
Software defect prediction is a critical component of software quality assurance. As the scale and complexity of software continue to increase, traditional methods based on manual review have become inadequate to meet the growing demands. This study aims to construct an efficient and accurate software defect prediction model to assist developers in identifying potential defective modules at an early stage. By collecting data from multiple open-source project repositories, static code analysis techniques were employed to extract feature variables, including code complexity, change frequency, and other multi-dimensional metrics. A deep learning algorithm was introduced for modeling. Compared with traditional machine learning methods, the proposed model based on convolutional neural networks can automatically learn feature representations without the need for manual feature engineering, thereby enhancing the generalization ability and prediction accuracy of the model. Experimental results demonstrate that this model significantly outperforms existing methods across various evaluation metrics, particularly achieving an F1-score above 0.85. Furthermore, interpretability analysis of the model's internal structure reveals strong correlations between certain specific code features and defect occurrence, providing new insights into understanding the causes of software defects. This research not only offers innovative solutions for the field of software defect prediction but also lays a theoretical foundation and technical support for future studies.
Keyword:Software Defect Prediction Convolutional Neural Network Static Code Analysis
目 录
引言 1
1软件缺陷预测的理论基础 1
1.1软件缺陷概述 1
1.2预测模型的基本原理 2
1.3常用预测方法综述 2
2数据收集与预处理 3
2.1数据来源与获取方式 3
2.2数据清洗与质量评估 3
2.3特征选择与工程 4
3模型构建与算法选择 4
3.1模型架构设计原则 4
3.2算法对比与选型 5
3.3参数调优策略 6
4模型验证与效果评估 6
4.1验证框架搭建 6
4.2性能指标分析 7
4.3结果讨论与改进 7
结论 8
参考文献 9
致谢 9
