基于机器学习的软件需求分析优化
摘 要
随着软件系统日益复杂，传统需求分析方法面临诸多挑战，难以高效准确地处理海量需求数据。基于机器学习的软件需求分析优化旨在解决这一问题，通过引入先进的机器学习算法提升需求分析的质量和效率。本研究聚焦于构建一个集成多种机器学习模型的需求分析框架，该框架能够自动识别需求文档中的关键信息，对功能性和非功能性需求进行精准分类，并预测潜在的风险点。研究采用深度神经网络、支持向量机和支持向量回归等算法，结合自然语言处理技术，对来自多个领域的实际需求文档进行训练与测试。实验结果表明，所提出的框架在需求分类准确性上较传统方法提高了约15%，风险预测的召回率提升了20%。此外，该框架还具备良好的可扩展性，能够适应不同规模和类型的项目需求。
关键词：机器学习 需求分析框架 多模态模型
Abstract
With the increasing complexity of software systems, the traditional demand analysis methods face many challenges, and it is difficult to process the massive demand data efficiently and accurately. Machine learning-based software demand analysis optimization aims to solve this problem by improving the quality and efficiency of demand analysis by introducing advanced machine learning algorithms. This study focuses on building a requirements analysis fr amework that integrates multiple machine learning models to automatically identify key information in requirements documents, accurately classify functional and non-functional needs, and predict potential risk points. The study uses deep neural network, support vector machine and support vector regression algorithms, combined with natural language processing technology, to train and test the practical demand documents from multiple fields. The experimental results show that the proposed fr amework improves the requirement classification accuracy by about 15% compared with the traditional method, and the recall rate of risk prediction increases by 20%. In addition, the fr amework also has good scalability and can adapt to the project needs of different sizes and types.
Keyword:Machine Learning Requirements Analysis fr amework Multimodal Model
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 1
2机器学习在需求获取中的应用 2
2.1需求获取的挑战 2
2.2机器学习算法选择 3
2.3提升需求获取准确性 3
3需求分析模型的构建与优化 4
3.1模型构建原则 4
3.2特征工程的应用 4
3.3模型性能评估 5
4需求变更管理的智能化 6
4.1变更预测机制 6
4.2自动化影响分析 6
4.3变更决策支持 7
结论 8
参考文献 9
致谢 10
摘 要
随着软件系统日益复杂，传统需求分析方法面临诸多挑战，难以高效准确地处理海量需求数据。基于机器学习的软件需求分析优化旨在解决这一问题，通过引入先进的机器学习算法提升需求分析的质量和效率。本研究聚焦于构建一个集成多种机器学习模型的需求分析框架，该框架能够自动识别需求文档中的关键信息，对功能性和非功能性需求进行精准分类，并预测潜在的风险点。研究采用深度神经网络、支持向量机和支持向量回归等算法，结合自然语言处理技术，对来自多个领域的实际需求文档进行训练与测试。实验结果表明，所提出的框架在需求分类准确性上较传统方法提高了约15%，风险预测的召回率提升了20%。此外，该框架还具备良好的可扩展性，能够适应不同规模和类型的项目需求。
关键词：机器学习 需求分析框架 多模态模型
Abstract
With the increasing complexity of software systems, the traditional demand analysis methods face many challenges, and it is difficult to process the massive demand data efficiently and accurately. Machine learning-based software demand analysis optimization aims to solve this problem by improving the quality and efficiency of demand analysis by introducing advanced machine learning algorithms. This study focuses on building a requirements analysis fr amework that integrates multiple machine learning models to automatically identify key information in requirements documents, accurately classify functional and non-functional needs, and predict potential risk points. The study uses deep neural network, support vector machine and support vector regression algorithms, combined with natural language processing technology, to train and test the practical demand documents from multiple fields. The experimental results show that the proposed fr amework improves the requirement classification accuracy by about 15% compared with the traditional method, and the recall rate of risk prediction increases by 20%. In addition, the fr amework also has good scalability and can adapt to the project needs of different sizes and types.
Keyword:Machine Learning Requirements Analysis fr amework Multimodal Model
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 1
2机器学习在需求获取中的应用 2
2.1需求获取的挑战 2
2.2机器学习算法选择 3
2.3提升需求获取准确性 3
3需求分析模型的构建与优化 4
3.1模型构建原则 4
3.2特征工程的应用 4
3.3模型性能评估 5
4需求变更管理的智能化 6
4.1变更预测机制 6
4.2自动化影响分析 6
4.3变更决策支持 7
结论 8
参考文献 9
致谢 10
