发表机构
ISISTAN Research Institute, CONICET-UNCPBA; Johannes Kepler University（ISISTAN研究所，CONICET-UNCPBA; 约翰内斯·开普勒大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合主题建模、LLM分类和模板检查的自动化文本挖掘方法，用于大规模分析约550个开源仓库中的架构决策记录，发现其常记录存在性、技术和流程决策，而备选方案等记录不足，为架构师改进实践提供参考。
AI 中文摘要
架构决策记录（ADR）已成为在软件项目中记录架构知识的一种流行的轻量级机制。然而，关于ADR中捕获的架构关注点类型及其内容与既定架构知识概念和文档实践的一致性，目前仍缺乏实证证据。在本文中，我们提出了一种自动化的文本挖掘与分类方法，用于大规模分析ADR。我们将该方法应用于从约550个开源代码库中提取的ADR数据集，结合了主题建模、基于LLM的分类和模板合规性检查。我们的分析考察了决策分类法和质量属性，以及ADR遵循MADR模板的程度。我们的研究结果表明，ADR经常捕获存在性、技术和流程相关的决策，而备选方案、决策驱动因素和一些质量关注点则记录不足。我们还观察到ADR内容与模板部分之间反复出现的不匹配。这些对当前文档实践的洞察为架构师提供了有价值的信息，以反思ADR如何被使用以及应当如何使用，从而有效处理架构知识。此外，我们的自动化方法可适用于其他架构任务。
英文摘要
Architectural decision records (ADRs) have become a popular lightweight mechanism for documenting architectural knowledge in software projects. However, there is limited empirical evidence on the kinds of architectural concerns captured in ADRs and how well their contents align with established architectural knowledge concepts and documentation practices. In this paper, we propose an automated text-mining and classification approach for analyzing ADRs at scale. We apply this approach to a dataset of ADRs extracted from ~550 open-source repositories, combining topic modeling, LLM-based classification, and template compliance checks. Our analysis examines decision taxonomies and quality attributes, and the degree to which ADRs adhere to the MADR template. Our findings show that ADRs frequently capture existence, technology, and process-related decisions, while alternatives, decisions drivers, and some quality concerns remain under-documented. We also observe recurring mismatches between ADR contents and template sections. These insights into current documentation practices provide architects with valuable information to reflect on how ADRs are and should be used to effectively deal with architectural knowledge. Furthermore, our automated approach is adaptable to other architectural tasks.
