System testing is essential in any software development project to ensure that the final products meet the requirements. Creating comprehensive test cases for system testing from requirements is often challenging and time-consuming. This paper explores the effectiveness of using Large Language Models (LLMs) to generate test case designs from Software Requirements Specification (SRS) documents. In this study, we collected the SRS documents of five software engineering projects containing functional and non-functional requirements, which were implemented, tested, and delivered by respective developer teams. For generating test case designs, we used ChatGPT-4o Turbo model. We employed prompt-chaining, starting with an initial context-setting prompt, followed by prompts to generate test cases for each use case. We assessed the quality of the generated test case designs through feedback from the same developer teams as mentioned above. Our experiments show that about 87 percent of the generated test cases were valid, with the remaining 13 percent either not applicable or redundant. Notably, 15 percent of the valid test cases were previously not considered by developers in their testing. We also tasked ChatGPT with identifying redundant test cases, which were subsequently validated by the respective developers to identify false positives and to uncover any redundant test cases that may have been missed by the developers themselves. This study highlights the potential of leveraging LLMs for test generation from the Requirements Specification document and also for assisting developers in quickly identifying and addressing redundancies, ultimately improving test suite quality and efficiency of the testing procedure.

翻译：系统测试在任何软件开发项目中都至关重要，以确保最终产品满足需求。根据需求为系统测试创建全面的测试用例通常具有挑战性且耗时。本文探讨了使用大型语言模型从软件需求规格说明文档生成测试用例设计的有效性。在本研究中，我们收集了五个软件工程项目的SRS文档，这些文档包含功能性和非功能性需求，并由相应的开发团队实现、测试和交付。为生成测试用例设计，我们使用了ChatGPT-4o Turbo模型。我们采用了提示链方法，从初始上下文设置提示开始，随后针对每个用例生成测试用例的提示。我们通过上述同一开发团队的反馈评估了生成的测试用例设计的质量。实验表明，约87%的生成测试用例是有效的，其余13%要么不适用，要么是冗余的。值得注意的是，15%的有效测试用例是开发人员在其测试中先前未考虑的。我们还让ChatGPT识别冗余测试用例，随后由相应开发人员验证以识别误报，并发现开发人员自身可能遗漏的任何冗余测试用例。本研究凸显了利用LLM从需求规格说明文档生成测试用例以及协助开发人员快速识别和解决冗余问题的潜力，最终提升测试套件质量和测试过程的效率。