自动从开发者社区提取架构问题与解决方案对，提升知识获取效率。
ArchISMiner: A Framework for Automatic Mining of Architectural Issue-Solution Pairs from Online Developer Communities
- 用多模型对比筛选最优分类器，精准识别架构相关帖子
- 通过混合特征提取，实现问题-方案对的高精度抽取（F1达0.883/0.894）
- 适用于架构师和开发者快速获取真实场景下的设计经验
Stack Overflow（SO）作为领先的在线开发者社区，蕴含丰富的软件开发知识。然而，由于内容高度非结构化且讨论碎片化，定位架构知识仍具挑战性，开发者需手动筛选，耗时且易出错。本文提出ArchISMiner框架，包含两个互补模块：ArchPI通过训练评估多种模型（包括传统机器学习、深度学习、预训练语言模型及大语言模型），选择最优模型自动识别编程讨论中的架构相关帖子（ARPs）；ArchISPE采用间接监督方法，结合BERT嵌入与局部TextCNN特征，提取架构问题-解决方案对。评估显示，ArchPI最佳模型在ARP检测中达到0.960的F1值，ArchISPE在软件工程与自然语言处理领域基线之上，分别实现0.883（问题）和0.894（解决方案）的F1值。用户研究验证了识别结果的相关性与实用性。此外，该框架应用于三个额外论坛，构建了超过18,000条架构问题-解决方案对的数据集。整体而言，ArchISMiner可更准确高效地帮助架构师与开发者从社区中提取有价值的架构知识。本研究的复现包已发布于https://github.com/JeanMusenga/ArchISPE。
原文摘要 · Abstract (English)
Stack Overflow (SO), a leading online community forum, is a rich source of software development knowledge. However, locating architectural knowledge, such as architectural solutions remains challenging due to the overwhelming volume of unstructured content and fragmented discussions. Developers must manually sift through posts to find relevant architectural insights, which is time-consuming and error-prone. This study introduces ArchISMiner, a framework for mining architectural knowledge from SO. The framework comprises two complementary components: ArchPI and ArchISPE. ArchPI trains and evaluates multiple models, including conventional ML/DL models, Pre-trained Language Models (PLMs), and Large Language Models (LLMs), and selects the best-performing model to automatically identify Architecture-Related Posts (ARPs) among programming-related discussions. ArchISPE employs an indirect supervised approach that leverages diverse features, including BERT embeddings and local TextCNN features, to extract architectural issue-solution pairs. Our evaluation shows that the best model in ArchPI achieves an F1-score of 0.960 in ARP detection, and ArchISPE outperforms baselines in both SE and NLP fields, achieving F1-scores of 0.883 for architectural issues and 0.894 for solutions. A user study further validated the quality (e.g., relevance and usefulness) of the identified ARPs and the extracted issue-solution pairs. Moreover, we applied ArchISMiner to three additional forums, releasing a dataset of over 18K architectural issue-solution pairs. Overall, ArchISMiner can help architects and developers identify ARPs and extract succinct, relevant, and useful architectural knowledge from developer communities more accurately and efficiently. The replication package of this study has been provided at https://github.com/JeanMusenga/ArchISPE
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