摘 要
随着信息技术的迅猛发展，软件系统日益复杂，传统需求分析方法面临诸多挑战，如需求获取不全面、变更频繁难以应对等。大数据技术凭借其处理海量数据的能力为软件需求分析提供了新的思路。本研究旨在探索大数据技术在软件需求分析中的应用，以提高需求分析的质量和效率。通过文献调研与案例分析相结合的方法，深入剖析大数据技术在需求获取、需求建模以及需求验证等环节的应用潜力。研究发现，利用大数据技术可以从多源异构数据中挖掘潜在用户需求，构建更精准的需求模型，并实现对需求变更的有效预测。创新性地提出基于大数据分析的需求优先级评估框架，该框架能够综合考虑多种因素动态调整需求优先级，确保关键需求得到及时响应。此外，构建了需求变更影响分析模型，借助大数据相关算法准确评估需求变更带来的影响范围及程度，从而降低变更风险。
关键词：大数据技术 软件需求分析 需求优先级评估
Abstract
With the rapid development of information technology, the software system is increasingly complex, and the traditional demand analysis methods face many challenges, such as incomplete demand acquisition and difficult to cope with frequent changes. Big data technology provides new ideas for software demand analysis with its ability to process massive data. This study aims to explore the application of big data technology in software demand analysis to improve the quality and efficiency of demand analysis. Through the combination of literature research and case analysis, the application potential of big data technology in demand acquisition, demand modeling and demand verification is deeply analyzed. It is found that the use of big data technology can mine potential user needs from multi-source heterogeneous data, build a more accurate demand model, and realize the effective prediction of demand changes.innovatively propose a demand priority assessment fr amework based on big data analysis, which can dynamically adjust demand priorities taking into account multiple factors to ensure that key needs are responded to in a timely manner. In addition, the impact analysis model of demand change is constructed to accurately evaluate the scope and extent of the impact of demand change with the help of big data related algorithms, so as to reduce the risk of change.
Keyword:Big Data Technology Software Requirements Analysis Requirements Priority Evaluation
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研究方法概述 1
2大数据技术基础及其应用环境 2
2.1大数据技术核心概念 2
2.2软件需求分析中的大数据来源 3
2.3大数据处理平台对需求分析的支持 3
3需求获取中的大数据应用 4
3.1用户行为数据的收集与分析 4
3.2社交媒体数据挖掘助力需求发现 4
3.3多源异构数据融合提升需求准确性 5
4需求建模与验证中的大数据技术 5
4.1基于大数据的需求模型构建 6
4.2数据驱动的需求变更预测 6
4.3利用大数据进行需求验证优化 7
结论 7
参考文献 9
致谢 10
随着信息技术的迅猛发展，软件系统日益复杂，传统需求分析方法面临诸多挑战，如需求获取不全面、变更频繁难以应对等。大数据技术凭借其处理海量数据的能力为软件需求分析提供了新的思路。本研究旨在探索大数据技术在软件需求分析中的应用，以提高需求分析的质量和效率。通过文献调研与案例分析相结合的方法，深入剖析大数据技术在需求获取、需求建模以及需求验证等环节的应用潜力。研究发现，利用大数据技术可以从多源异构数据中挖掘潜在用户需求，构建更精准的需求模型，并实现对需求变更的有效预测。创新性地提出基于大数据分析的需求优先级评估框架，该框架能够综合考虑多种因素动态调整需求优先级，确保关键需求得到及时响应。此外，构建了需求变更影响分析模型，借助大数据相关算法准确评估需求变更带来的影响范围及程度，从而降低变更风险。
关键词：大数据技术 软件需求分析 需求优先级评估
Abstract
With the rapid development of information technology, the software system is increasingly complex, and the traditional demand analysis methods face many challenges, such as incomplete demand acquisition and difficult to cope with frequent changes. Big data technology provides new ideas for software demand analysis with its ability to process massive data. This study aims to explore the application of big data technology in software demand analysis to improve the quality and efficiency of demand analysis. Through the combination of literature research and case analysis, the application potential of big data technology in demand acquisition, demand modeling and demand verification is deeply analyzed. It is found that the use of big data technology can mine potential user needs from multi-source heterogeneous data, build a more accurate demand model, and realize the effective prediction of demand changes.innovatively propose a demand priority assessment fr amework based on big data analysis, which can dynamically adjust demand priorities taking into account multiple factors to ensure that key needs are responded to in a timely manner. In addition, the impact analysis model of demand change is constructed to accurately evaluate the scope and extent of the impact of demand change with the help of big data related algorithms, so as to reduce the risk of change.
Keyword:Big Data Technology Software Requirements Analysis Requirements Priority Evaluation
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研究方法概述 1
2大数据技术基础及其应用环境 2
2.1大数据技术核心概念 2
2.2软件需求分析中的大数据来源 3
2.3大数据处理平台对需求分析的支持 3
3需求获取中的大数据应用 4
3.1用户行为数据的收集与分析 4
3.2社交媒体数据挖掘助力需求发现 4
3.3多源异构数据融合提升需求准确性 5
4需求建模与验证中的大数据技术 5
4.1基于大数据的需求模型构建 6
4.2数据驱动的需求变更预测 6
4.3利用大数据进行需求验证优化 7
结论 7
参考文献 9
致谢 10
