摘要
为使理论有效指导实践以提高需求工程质量,了解工业界对需求工程的关注点是十分有必要的。为此,提出了基于数据挖掘的4步研究框架。首先筛选合适的工业界数据源,包括博客类和问答类网站,然后确定合适的关键词以爬取数据并进行清洗,随后根据不同的数据特点进行相似度分析和数据标注处理,最后完成数据统计分析。研究结果表明,国内外工业界对需求工程的关注点各有异同。国内外都关注敏捷需求;国内外都关注用户故事和用例的区别,其潜在反映了实践中传统和敏捷混合开发模式下的需求实践问题;国内外都关注实践中工具的应用,虽然国内使用工具种类多样,但自主开发的工具相对少;国内工业界还关注需求工程的概念和方法,以及需求工程师的职业发展,但国外基本不关注。此外,国内实践中关注需求分析多于需求变更,还关注与需求相关的测试和项目管理领域。该研究结果可有效指引需求工程相关理论在工业界的应用,以解决实践中的难点,并为学术界和工业界提供了可能的研究和发展方向。
In order to effectively guide theory into practice and further improve the quality of requirements engineering(RE),it is necessary to understand the focuses of RE in industry.To solve this problem,this paper proposes a research scheme with four steps based on data mining.Firstly,suitable data sources are selected,including blogs and Q&A websites.Secondly,suitable keywords are determined,and data related to RE,are crawled and cleaned.Then,according to the characters of different data,text similarity analysis and label data are conducted.Finally,data analysis are done.The research results show that the focuses of RE between domestic and foreign industry have similarities and differences.Both domestic and foreign industries focus on agile requirements,and both concern the difference between user story and use case,which potentially reflects the requirements issue of hybrid development combing traditional with agile in practice.The applications of RE tools are concerned by both,and,although the types of RE tools used in domestic practice are multiple,tools developed by domestic companies are relatively few.The concepts and methods of RE and the career development of requirements engineers are the focuses in domestic industry,but not in foreign industry.In addition,domestic industry pays more attention to requirements analysis than requirements change,and two fields(test and project management)related to RE are also focused on in domestic industry.The research results can effectively guide the application of related RE theory into focuses in industry,so as to solve the difficulties in RE practice,and provide possible research and development directions for academia and industry.
作者
贾经冬
张筱曼
郝璐
谭火彬
JIA Jing-dong;ZHANG Xiao-man;HAO Lu;TAN Huo-bin(School of Software,Beihang University,Beijing 100083,China)
出处
《计算机科学》
CSCD
北大核心
2020年第12期25-34,共10页
Computer Science
基金
国家重点研发计划(2018YFB1402600)。
关键词
需求工程
工业界
文本相似度分析
数据标注
数据分析
Requirements engineering
Industry
Text similarity analysis
Data label
Data analysis