Do Large Language Models Contain Software Architectural Knowledge? An Exploratory Case Study with GPT

Architectural knowledge (AK) of existing systems is essential for software engineers to make design decisions. Recently, Large Language Models (LLMs) trained on large-scale datasets, including software repositories, have shown promise in embedding knowledge and answering questions. However, LLMs have not been evaluated for their abilities to answer questions about AK, leaving doubts about their accuracy. This paper assesses GPT, a leading LLM, by evaluating its responses' accuracy, quality, and trustworthiness on the AK of the large-scale open-source system HDFS. We conducted an exploratory case study with 14 software engineers who posed questions to GPT and compared its responses to a predefined ground truth. The engineers rated GPT’s answers with moderate quality and trustworthiness. Our findings on GPT´s accuracy indicates moderate recall but lower precision, especially in identifying quality attribute solutions and design rationales. These results suggest that while GPT and similar models can provide initial insights into AK, expert validation remains necessary for reliability. This study underscores LLMs' potential and limitations to discover software AK.