Musengamana Jean de Dieu

Musengamana Jean de Dieu, Ruiyin Li, Peng Liang et al.

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

Musengamana Jean de Dieu, Peng Liang, Mojtaba Shahin

Building software systems often requires knowledge and skills beyond what developers already possess. In such cases, developers have to leverage different sources of information to seek help. A growing number of researchers and practitioners have started investigating what programming-related information developers seek during software development. However, being a high level and a type of the most important development-related information, architectural information search activity is seldom explored. To fill this gap, we conducted an industrial survey completed by 103 participants to understand how developers search for architectural information to solve their architectural problems in development. Our main findings are: (1) searching for architectural information to learn about the pros and cons of certain architectural solutions (e.g., patterns, tactics) and to make an architecture decision among multiple choices are the most frequent purposes or tasks; (2) developers find difficulties mostly in getting relevant architectural information for addressing quality concerns and making design decisions among multiple choices when seeking architectural information; (3) taking too much time to go through architectural information retrieved from various sources and feeling overwhelmed due to the dispersion and abundance of architectural information in various sources are the top two major challenges developers face when searching for architectural information. Our findings (1) provide researchers with future directions, such as the design and development of approaches and tools for searching architectural information from multiple sources, and (2) can be used to provide guidelines for practitioners to refer to when seeking architectural information and providing architectural information that could be considered useful.