ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (4): 683-705.doi: 10.7544/issn1000-1239.2021.20200740

所属专题: 2021人工智能背景下的需求工程专题

• 人工智能 • 上一篇    下一篇

智能需求获取与建模研究综述

汪烨1,陈骏武1,夏鑫2,姜波1   

  1. 1(浙江工商大学计算机与信息工程学院 杭州 310018);2(澳大利亚蒙纳士大学信息技术学院 澳大利亚墨尔本 3800) (yewang@zjgsu.edu.cn)
  • 出版日期: 2021-04-01
  • 基金资助: 
    浙江省自然科学基金项目(LY21F020011,LY20F020027,LY19F020003);国家自然科学基金项目(61672459)

Intelligent Requirements Elicitation and Modeling: A Literature Review

Wang Ye1, Chen Junwu1, Xia Xin2, Jiang Bo1   

  1. 1(School of Computer and Information Engineering,Zhejiang Gongshang University,Hangzhou 310018);2(Faculty of Information Technology,Monash University,Melbourne,Australia 3800)
  • Online: 2021-04-01
  • Supported by: 
    This work was supported by the Natural Science Foundation of Zhejiang Province (LY21F020011, LY20F020027, LY19F020003) and the National Natural Science Foundation of China (61672459).

摘要: 需求获取和建模是指从需求文本或记录中获取显式和隐式的需求,并通过表格化、图形化、形式化等方法构建相应模型的过程,是软件开发过程中极为关键的一步,为后续系统设计与实现铺平道路,提高软件开发效率和质量,提升软件系统稳定性和可行性.研究者们在需求获取与建模方面获得了一系列研究成果,根据其关注阶段不同,可以将它们分为需求知识提取、需求知识分类和需求模型构建3个方面.鉴于传统方法在知识获取、模型构建的准确性和效率方面一直存在弊端,近年来,越来越多的研究者们将具有广泛应用性的人工智能技术与需求获取、需求分类、需求建模方法相结合,提出了一系列智能需求获取与建模的方法和技术,从而弥补了传统方法的不足.着重从智能需求获取与建模角度着手,对近年来的研究进展进行梳理和总结.主要内容包括:1)统计并分析人工智能技术在需求知识提取、需求知识分类和需求模型构建中使用的方法和技术;2)总结了智能需求获取与建模过程中采用的验证方法和评估方法;3)从科学问题和技术难点2个方面归纳得出目前智能需求获取与建模的关键问题,围绕集成式和动态化模型构建、与其他软件工程活动关联、智能需求知识分类的粒度、数据集构建、评估指标构建和工具支持6部分,阐述了上述问题的可能解决思路和未来发展趋势.

关键词: 需求建模, 人工智能, 需求获取, 自然语言处理, 机器学习, 深度学习

Abstract: Requirements elicitation and modeling refer to the process of obtaining explicit or implicit requirements from the requirements text described in natural language, and constructing the corresponding models through tabular, graphical, and formulaic methods. Requirements elicitation and modeling is an extremely critical step in software development process, which paves the way for subsequent system design and implementation, improves the efficiency and quality of software development, and improves the stability and feasibility of software systems. Researchers have obtained a series of research achievements in requirements elicitation and modeling. Requirements elicitation and modeling can be generally divided into three steps: requirements knowledge extraction, requirements knowledge classification and requirements model construction. Due to the fact that traditional requirements elicitation and modeling approaches have problems in terms of accuracy and efficiency of model construction, in recent years, more and more researchers have integrated widely applicable artificial intelligence techniques with these approaches, and put forward a series of intelligent require-ments elicitation and modeling approaches, so as to make up for the deficiencies of the traditional methods. This paper focuses on the perspective of intelligent requirements elicitation and modeling, and sorts out and summarizes the research progress of requirements elicitation and modeling in recent years. The main contents include: 1)statistics and analysis of the artificial intelligence techniques applied in requirements knowledge extraction, requirements knowledge classification and requirements model construction; 2)summarizing the verification and evaluation methods used in the process of intelligent requirements elicitation and modeling; 3)summarizing the key issues of intelligent require-ments elicitation and modeling from two aspects of scientific problems and technical difficulties, and elaborating on the six research trends including integrated and dynamic model construction, mining the relationships among intelligent requirements elicitation and modeling and other software engineering activities, refining the granularity of intelligent requirements modeling, data sets construction, evaluation metrics construction and industrial practice as the possible solutions to the above problems. The future development trend of intelligent requirements elicitation and modeling research is also discussed.

Key words: requirements modeling, artificial intelligence, requirements elicitation, natural language processing, machine learning, deep learning

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