摘 要
随着信息技术的迅猛发展，软件系统规模日益庞大且复杂，传统软件工程方法在需求分析、质量保障、维护优化等方面面临诸多挑战。本研究旨在探讨数据挖掘技术在软件工程领域的应用潜力，以期通过智能化手段提升软件开发效率与质量。基于此目标，本文采用文献综述与实证分析相结合的方法，系统梳理了数据挖掘算法（如分类、聚类、关联规则等）在软件生命周期各阶段的应用场景。通过对多个开源项目的数据集进行实验验证，结果表明数据挖掘技术能够有效识别代码缺陷模式、预测软件故障风险、优化需求变更管理流程。特别是在自动化测试用例生成和持续集成环境中展现出显著优势。本研究创新性地提出了基于深度学习的缺陷定位模型，该模型融合了静态代码特征与动态运行时信息，实现了比传统方法更高的准确率。此外，还构建了一套完整的软件健康评估指标体系，为量化评估软件质量提供了新思路。
关键词：数据挖掘技术 软件工程 缺陷定位模型
Abstract
With the rapid development of information technology, the scale of software system is increasingly large and complex, and the traditional software engineering methods face many challenges in demand analysis, quality assurance, maintenance optimization and other aspects. The purpose of this study is to explore the application potential of data mining technology in software engineering, in order to improve the efficiency and quality of software development through intelligent means. Based on this goal, this paper adopts the method of combining literature review and empirical analysis to systematically comb the application scenarios of data mining algorithms (such as classification, clustering, association rules, etc.) in each stage of the software life cycle. Through the experimental verification of data sets of several open source projects, the results show that data mining technology can effectively identify code defect patterns, predict software failure risk, and optimize the demand change management process. In particular, it shows significant advantages in automated test case generation and continuous integration environments. This study innovatively proposes a defect localization model based on deep learning, which integrates static code features and dynamic runtime information to achieve higher accuracy than traditional methods. In addition, a complete set of software health evaluation index system is also constructed, which provides a new idea for the quantitative evaluation of software quality.
Keyword:Data Mining Technology Software Engineering Defect Localization Model
目 录
1绪论 1
1.1数据挖掘与软件工程的背景意义 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2数据挖掘技术在需求分析中的应用 2
2.1需求预测模型构建 2
2.2用户行为模式挖掘 3
2.3需求变更趋势分析 3
3数据挖掘提升软件设计质量的方法 4
3.1设计模式识别优化 4
3.3软件架构评估改进 5
4数据挖掘在软件测试中的应用探索 6
4.1测试用例生成优化 6
4.2故障定位与诊断 6
4.3测试效率提升策略 7
结论 7
参考文献 9
致谢 10
随着信息技术的迅猛发展，软件系统规模日益庞大且复杂，传统软件工程方法在需求分析、质量保障、维护优化等方面面临诸多挑战。本研究旨在探讨数据挖掘技术在软件工程领域的应用潜力，以期通过智能化手段提升软件开发效率与质量。基于此目标，本文采用文献综述与实证分析相结合的方法，系统梳理了数据挖掘算法（如分类、聚类、关联规则等）在软件生命周期各阶段的应用场景。通过对多个开源项目的数据集进行实验验证，结果表明数据挖掘技术能够有效识别代码缺陷模式、预测软件故障风险、优化需求变更管理流程。特别是在自动化测试用例生成和持续集成环境中展现出显著优势。本研究创新性地提出了基于深度学习的缺陷定位模型，该模型融合了静态代码特征与动态运行时信息，实现了比传统方法更高的准确率。此外，还构建了一套完整的软件健康评估指标体系，为量化评估软件质量提供了新思路。
关键词：数据挖掘技术 软件工程 缺陷定位模型
Abstract
With the rapid development of information technology, the scale of software system is increasingly large and complex, and the traditional software engineering methods face many challenges in demand analysis, quality assurance, maintenance optimization and other aspects. The purpose of this study is to explore the application potential of data mining technology in software engineering, in order to improve the efficiency and quality of software development through intelligent means. Based on this goal, this paper adopts the method of combining literature review and empirical analysis to systematically comb the application scenarios of data mining algorithms (such as classification, clustering, association rules, etc.) in each stage of the software life cycle. Through the experimental verification of data sets of several open source projects, the results show that data mining technology can effectively identify code defect patterns, predict software failure risk, and optimize the demand change management process. In particular, it shows significant advantages in automated test case generation and continuous integration environments. This study innovatively proposes a defect localization model based on deep learning, which integrates static code features and dynamic runtime information to achieve higher accuracy than traditional methods. In addition, a complete set of software health evaluation index system is also constructed, which provides a new idea for the quantitative evaluation of software quality.
Keyword:Data Mining Technology Software Engineering Defect Localization Model
目 录
1绪论 1
1.1数据挖掘与软件工程的背景意义 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2数据挖掘技术在需求分析中的应用 2
2.1需求预测模型构建 2
2.2用户行为模式挖掘 3
2.3需求变更趋势分析 3
3数据挖掘提升软件设计质量的方法 4
3.1设计模式识别优化 4
3.3软件架构评估改进 5
4数据挖掘在软件测试中的应用探索 6
4.1测试用例生成优化 6
4.2故障定位与诊断 6
4.3测试效率提升策略 7
结论 7
参考文献 9
致谢 10
