ReqRAG: Enhancing Software Release Management through Retrieval-Augmented LLMs: An Industrial Study

[Context and Motivation] Engineers often need to refer back to release notes, manuals, and system architecture documents to understand, modify, or upgrade functionalities in alignment with new software releases. This is crucial to ensure that new stakeholder requirements align with the existing system, maintaining compatibility and preventing integration issues. [Problem] In practice, the manual process of retrieving the relevant information from technical documentation is time-intensive and frequently results in inefficient software release management. [Principal ideas/results] In this paper, we propose a question-answering chatbot, ReqRAG, leveraging Retrieval Augmented Generation (RAG) with Large Language Models (LLMs) to deliver accurate and up-to-date information from technical documents in response to given queries. We employ various context retrieval techniques paired with state-of-the-art LLMs to evaluate the ReqRAG approach in industrial settings. Furthermore, we conduct human evaluations of the results in collaboration with experts from Alstom to gain practical insights. Our results indicate that, on average, 70% of the generated responses are adequate, useful, and relevant to the practitioners. [Contribution] Fewer studies have comprehensively evaluated RAG-based approaches in industrial settings. Therefore, this work provides technical considerations for domain-specific chatbots, guiding researchers and practitioners facing similar challenges.