Supplementary Material for Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study

This repository delivers the supplementary material for the paper: <em>Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study.</em> In the following, we present the abstract of the study: <strong>Context:</strong> Agile Software Development (ASD) is a disruptive process compared to traditional software development. Therefore, traditional Requirements Engineering (RE) forms may not be the best way to do RE for ASD (RE-ASD). <strong>Objective:</strong> Working with ASD using traditional RE ways could limit ASD's potential. Thus, it is necessary to investigate what academia and industry have done in RE to take full advantage of all of the capabilities of ASD beyond traditional RE. <strong>Method: </strong>We conducted a Tertiary Study looking for solutions for RE-ASD using the Systematic Literature Review (SLR) protocol described by Kitchenham and Charters. We then categorized the solutions into families using Targeted Coding and Constant Comparison, tools from Socio-Technical Grounded Theory (STGT). Afterward, we classified the solutions as disruptive using our model based on the Hype Level Curve concept, assessing their hype (popularity) in the software engineering community using Google Trends and Google Colab tools. <strong>Results:</strong> After executing the SLR protocol, we accepted 37 studies and encountered 136 solutions used by academia and industry for RE-ASD. We categorized these solutions into 21 solution families, six of which we classified as disruptive. Design Thinking (DT) and Artificial Intelligence (AI) were the two families of solutions that stood out the most. We also identified the type of solution (e.g., process, method, technique, tool, model, framework) and domain (academia or industry). Furthermore, we cataloged the challenges presented by the solutions. <strong>Conclusion:</strong> We concluded that only a few solutions that have been used for RE-ASD have the power to successfully challenge the mainstream Agile Software Development process by using innovation (26 out of 106). There is a gap between academia and industry regarding these disruptive solutions, and some challenges still need to be addressed in using these solutions. The repository contains the following: Dataset from the Tertiary Study: Data of the retrieved studies. It presents the classifications of the documents as 'Accepted,' 'Rejected' (with the indication of the step of the protocol the authors rejected the study), or 'Duplicated.' Data of all solutions retrieved from the accepted studies Socio-Technical Grounded Theory (STGT) tools Result of the use of Targeted Coding and Constant Comparison The Google Colab Notebook Code in python Results

本仓库为论文《敏捷软件开发需求工程的颠覆性解决方案：一项三级研究》的补充材料。下文呈现该研究的摘要：**研究背景：** 敏捷软件开发（Agile Software Development, ASD）相较于传统软件开发属于颠覆性研发流程，因此传统需求工程（Requirements Engineering, RE）范式或许并非适配敏捷软件开发需求工程（Requirement Engineering for Agile Software Development, RE-ASD）的最优方案。**研究目标：** 采用传统需求工程方法开展敏捷软件开发工作，可能会限制敏捷软件开发的潜能。因此，有必要探究学术界与工业界在需求工程领域已开展的相关实践，以突破传统需求工程的桎梏，充分释放敏捷软件开发的全部效能。**研究方法：** 本研究遵循Kitchenham与Charters提出的系统文献综述（Systematic Literature Review, SLR）流程，针对RE-ASD的解决方案开展三级研究。随后，借助社会技术扎根理论（Socio-Technical Grounded Theory, STGT）中的目标编码法（Targeted Coding）与持续比较法（Constant Comparison），将所获解决方案划分为若干类别。之后，基于炒作度曲线（Hype Level Curve）概念构建评估模型，将这些解决方案划分为颠覆性方案；同时借助谷歌趋势（Google Trends）与谷歌Colab（Google Colab）工具，评估这些方案在软件工程社区中的受关注程度（即炒作热度）。**研究结果：** 执行系统文献综述流程后，最终纳入37项研究，从中提取得到学术界与工业界用于RE-ASD的136种解决方案。将这些解决方案划分为21个解决方案类别，其中6种被归类为颠覆性方案。设计思维（Design Thinking, DT）与人工智能（Artificial Intelligence, AI）是表现最为突出的两个解决方案类别。本研究同时明确了各类解决方案的类型（如流程、方法、技术、工具、模型、框架）与应用领域（学术界或工业界），此外还梳理了这些解决方案面临的各类挑战。**研究结论：** 本研究发现，当前用于RE-ASD的解决方案中，仅有少数（106种方案中的26种）能够通过创新手段有效挑战主流敏捷软件开发流程。在这些颠覆性解决方案方面，学术界与工业界之间仍存在一定差距，且部分应用挑战仍有待解决。本仓库包含以下内容：1. 三级研究相关数据集：检索得到的研究文献数据，包含文献的分类标签，如「已收录」「已排除」（并标注研究被排除时所处的综述流程步骤）以及「重复」；从已收录研究中提取的全部解决方案数据。2. 社会技术扎根理论（Socio-Technical Grounded Theory, STGT）工具应用成果：目标编码法与持续比较法的应用结果。3. 基于Python编写的谷歌Colab Notebook代码及运行结果。