Retrieval-Augmented Generation for Software Requirement-Based Test Case Generation
Zhiyao Wang, Xiujing Guo, Tatsuhiro Tsuchiya · 2025
Testers often need to manually write black-box test cases based on software artifacts such as requirement documents. In agile development, this process is often time-consuming and is further complicated by frequent requirement changes, leading to continuous maintenance overhead. Automating this process is therefore essential. Given the strong natural language understanding and generation capabilities of large language models (LLMs), combined with Retrieval-Augmented Generation (RAG), we propose a RAG-based framework for automated test case generation. Before generation, we embed software artifacts to construct a vectorbased knowledge database. At runtime, software requirements are used as queries to retrieve relevant context, which is integrated into a prompt and passed to the LLM for test case generation. This approach addresses several shortcomings of LLMs, including limited context length, attention dilution over large inputs, and the tendency to hallucinate or over-look key domain-specific constraints. By providing query-specific external knowledge, RAG enhances both accuracy and efficiency. We deploy the framework with different models locally and conduct experiments on two open-source datasets. Compared with the manually written benchmark test cases, our method achieves full requirement coverage with fewer test cases, improved efficiency, reduced error potential, and realized better readability.