Enhancing Large Language Models with Retrieval Augmented Generation for Software Testing and Inspection Automation
This work addresses the challenge of unreliable outputs from large language models in software test case generation and code review due to hallucination. It presents the first systematic integration of Retrieval-Augmented Generation (RAG) into software verification and validation activities. By constructing a RAG pipeline that dynamically incorporates contextual information from external knowledge bases, the approach effectively mitigates model hallucinations and enhances the accuracy and reliability of generated content. Experimental results demonstrate that this method significantly improves the quality of both test case generation and code review, reducing manual effort while strengthening the overall effectiveness of software verification processes.