摘要
随着软件开发的日益复杂，软件缺陷预测已成为提升软件质量的关键技术。软件缺陷，作为程序中存在的错误或不足，可能导致软件功能失效或性能下降，进而影响用户体验和系统稳定性。软件缺陷预测的流程涉及数据收集与标注、特征工程以及模型构建与应用三个核心环节。数据收集与标注是构建预测模型的基础，特征工程则负责从原始数据中提取关键信息，而模型构建与应用则是将学习到的知识应用于实际预测中。机器学习算法通过自动学习历史数据中的模式，能够有效预测软件模块中的潜在缺陷，从而提高软件开发的效率和质量。然而，机器学习在软件缺陷预测应用中仍面临诸多挑战，如数据缺失、模型泛化能力受限、模型解释性不足以及隐私和安全风险等。为了应对这些挑战，研究者们提出了多种应对策略。例如，采用专门处理缺失数据的模型，以增强模型的鲁棒性；通过增强数据集和正则化处理，提升模型的泛化能力；利用特征选择和可视化技术，增强模型的解释性；同时，对数据进行加密处理，以确保数据的安全性。这些策略的实施，将有助于提高机器学习在软件缺陷预测中的准确性和实用性。
关键词：机器学习；缺陷预测；模型泛化能力
Abstract
With the increasing complexity of software development, software defect prediction has become a key technology to improve software quality. Software defects, as the errors or deficiencies existing in the program, may lead to the software function failure or performance decline, thus affecting the user experience and system stability. The process of software defect prediction involves three core links: data collection and annotation, feature engineering, and model construction and application. Data collection and annotation are the basis of building prediction models, while feature engineering is responsible for extracting key information from the original data, while model construction and application is to apply the learned knowledge to the actual prediction. Machine learning algorithms can effectively predict the potential defects in software modules by automatically learning the patterns in historical data, so as to improve the efficiency and quality of software development. However, machine learning still faces many challenges in the application of software defect prediction, such as missing data, limited model generalization ability, insufficient model interpretability, and privacy and security risks. To meet these challenges, the researchers have proposed multiple coping strategies. For example, use models specifically for missing data to enhance model robustness; improve model generalization ability by enhancing data set and regularization processing; use feature selection and visualization techniques to enhance model interpretability; and encrypt data to ensure data security. The implementation of these strategies will help to improve the accuracy and utility of machine learning in software defect prediction.
Keywords： Machine learning; Defect prediction; Model generalization ability
目 录
摘要 I
Abstract II
一、绪论 1
（一）研究背景及意义 1
（二）国内外研究现状 1
二、软件缺陷预测概述 2
（一）软件缺陷的基本定义 2
（二）软件缺陷的常见分类 2
三、机器学习在软件缺陷预测中的应用 3
（一）缺陷定位 3
（二）缺陷检测 3
（三）质量评估 3
（四）模型训练与评估 4
四、机器学习在软件缺陷预测应用中面临的挑战 5
（一）数据缺失 5
（二）模型泛化能力受限 5
（三）模型解释性不足 5
（四）隐私和安全风险 6
五、机器学习在软件缺陷预测应用中的应对策略 6
（一）使用专门处理缺失数据的模型 6
（二）增强数据集与正则化处理 7
（三）增强模型解释性 7
（四）对数据进行加密 7
结 论 9
参考文献 10
