摘要
随着人工智能技术的快速发展，其在软件工程领域的应用逐渐成为研究热点。本研究旨在探索人工智能辅助决策系统在软件工程中的应用潜力，以提升软件开发效率和质量。研究背景基于当前软件工程面临的复杂性和不确定性挑战，传统方法难以满足快速变化的需求。为此，本文提出了一种融合机器学习与知识图谱的人工智能辅助决策框架，通过分析历史数据和领域知识，为需求分析、设计优化、代码生成及缺陷检测等关键环节提供智能化支持。研究采用实验验证与案例分析相结合的方法，构建了多个实际场景下的原型系统，并对其性能进行评估。结果表明，该框架能够显著提高软件开发过程中的决策准确性和效率，同时降低人为错误率。创新点在于将多源异构数据整合到统一的知识表示体系中，实现了跨阶段的智能化协同决策。主要贡献包括提出了适用于软件工程的通用人工智能辅助决策模型，以及为未来相关研究提供了理论基础和技术参考。研究成果对推动软件工程向智能化方向发展具有重要意义。
关键词：人工智能辅助决策；软件工程；机器学习；知识图谱；智能化协同决策
Abstract
With the rapid development of artificial intelligence technologies, their applications in software engineering have gradually become a research hotspot. This study aims to explore the potential of artificial-intelligence-aided decision-making systems in software engineering to enhance both the efficiency and quality of software development. The research background is rooted in the challenges of complexity and uncertainty faced by contemporary software engineering, where traditional methods struggle to meet the demands of rapid changes. To address these issues, this paper proposes an artificial-intelligence-aided decision-making fr amework that integrates machine learning with knowledge graphs. By analyzing historical data and domain-specific knowledge, the fr amework provides intelligent support for critical stages such as requirements analysis, design optimization, code generation, and defect detection. The study employs a combination of experimental validation and case analysis, constructing prototype systems in multiple real-world scenarios and evaluating their performance. The results demonstrate that the proposed fr amework significantly improves the accuracy and efficiency of decision-making during the software development process while reducing human error rates. The innovation lies in consolidating multi-source heterogeneous data into a unified knowledge representation system, enabling intelligent collaborative decision-making across different phases. Key contributions include the proposal of a general artificial-intelligence-aided decision-making model tailored for software engineering and the provision of a theoretical foundation and technical reference for future related research. The findings hold significant implications for advancing software engineering toward greater intelligence.
Keywords：Artificial Intelligence Assisted Decision; Software Engineering; Machine Learning; Knowledge Graph; Intelligent Collaborative Decision
目 录
摘要 I
Abstract II
一、绪论 1
(一) 软件工程与人工智能结合的背景 1
(二) 人工智能辅助决策系统的研究意义 1
(三) 当前研究现状与挑战 2
二、人工智能辅助决策系统的理论基础 2
(一) 软件工程中的关键概念解析 2
(二) 人工智能技术在决策支持中的作用 3
(三) 决策系统的核心算法与模型分析 3
(四) 理论框架对实践的指导意义 4
三、人工智能辅助决策系统的设计与实现 4
(一) 系统架构设计的关键要素 4
(二) 数据处理与模型训练的技术路径 5
(三) 决策支持功能的具体实现方式 5
(四) 设计过程中的优化与改进策略 6
四、应用场景与效果评估 6
(一) 软件开发流程中的应用实例 6
(二) 决策系统性能的量化评估方法 7
(三) 用户反馈与系统优化建议 7
(四) 实践中面临的挑战与未来方向 8
结 论 9
参考文献 10
