摘要
随着软件工程领域的快速发展，人工智能（AI）与机器学习（ML）技术逐渐成为提升软件开发效率、质量及智能化水平的重要手段。本研究旨在系统性地综述人工智能与机器学习在软件工程中的应用现状、关键技术和未来发展方向。通过广泛收集和分析近年来相关领域的文献资料，本文从需求分析、设计建模、代码生成、测试优化以及运维管理等多个维度探讨了AI与ML技术的具体应用场景及其带来的变革性影响。研究表明，基于机器学习的预测模型能够显著提高缺陷检测的准确性，自然语言处理技术为需求理解与文档自动生成提供了新思路，而深度学习方法则在智能代码补全和架构设计优化中展现出巨大潜力。此外，强化学习在自动化测试策略生成方面也取得了初步成果。本文的主要贡献在于首次提出了一个全面的分类框架，将AI与ML技术在软件工程中的应用划分为数据驱动型、知识增强型和自主决策型三类，并揭示了当前研究中存在的局限性，如数据质量不足、模型可解释性差以及领域适配性问题。最后，文章展望了未来可能的研究方向，包括多模态数据融合、联邦学习在隐私保护中的应用以及跨领域知识迁移等，为后续研究提供了理论参考与实践指导。
关键词：人工智能；机器学习；软件工程；预测模型；自然语言处理
Abstract
With the rapid development of software engineering, artificial intelligence (AI) and machine learning (ML) technologies have gradually become crucial approaches to enhancing the efficiency, quality, and intelligence level of software development. This study aims to systematically review the current applications, key technologies, and future directions of AI and ML in software engineering. By extensively collecting and analyzing literature from recent years, this paper explores the specific application scenarios and transformative impacts of AI and ML technologies across multiple dimensions, including requirements analysis, design modeling, code generation, testing optimization, and operation management. The findings indicate that prediction models based on machine learning can significantly improve the accuracy of defect detection, while natural language processing technology offers new insights into requirement understanding and automatic document generation. Additionally, deep learning methods demonstrate substantial potential in intelligent code completion and architecture design optimization, and reinforcement learning has achieved preliminary results in generating automated testing strategies. A major contribution of this paper is the proposal of a comprehensive classification fr amework that divides the applications of AI and ML in software engineering into three categories: data-driven, knowledge-enhanced, and autonomous decision-making. Furthermore, the study highlights existing limitations, such as insufficient data quality, poor model interpretability, and domain adaptability issues. Finally, the paper outlines potential future research directions, including multimodal data fusion, the application of federated learning for privacy protection, and cross-domain knowledge transfer, providing theoretical references and practical guidance for subsequent studies.
Keywords：Artificial Intelligence; Machine Learning; Software Engineering; Prediction Model; Natural Language Processing
目 录
摘要 I
Abstract II
一、绪论 1
(一) 软件工程与AI结合的背景与意义 1
(二) 当前研究现状与发展趋势 1
(三) 本文研究方法与技术路线 2
二、AI在软件需求分析中的应用 2
(一) 自然语言处理助力需求提取 2
(二) 机器学习优化需求优先级评估 3
(三) 数据驱动的需求变更管理 3
三、AI在软件设计与开发中的作用 4
(一) 智能代码生成与推荐系统 4
(二) 基于AI的设计模式识别与优化 4
(三) 自动化测试用例生成技术 5
四、AI在软件维护与管理中的实践 5
(一) 故障预测与异常检测算法 5
(二) 智能化版本控制与更新策略 6
(三) 数据挖掘支持的性能优化方法 7
结 论 8
参考文献 9
