In the contemporary landscape of technological advancements, the automation of manual processes is crucial, compelling the demand for huge datasets to effectively train and test machines. This research paper is dedicated to the exploration and implementation of an automated approach to generate test cases specifically using Large Language Models. The methodology integrates the use of Open AI to enhance the efficiency and effectiveness of test case generation for training and evaluating Large Language Models. This formalized approach with LLMs simplifies the testing process, making it more efficient and comprehensive. Leveraging natural language understanding, LLMs can intelligently formulate test cases that cover a broad range of REST API properties, ensuring comprehensive testing. The model that is developed during the research is trained using manually collected postman test cases or instances for various Rest APIs. LLMs enhance the creation of Postman test cases by automating the generation of varied and intricate test scenarios. Postman test cases offer streamlined automation, collaboration, and dynamic data handling, providing a user-friendly and efficient approach to API testing compared to traditional test cases. Thus, the model developed not only conforms to current technological standards but also holds the promise of evolving into an idea of substantial importance in future technological advancements.

翻译：在当代技术进步的背景下，手动流程的自动化至关重要，这迫使人们需要大规模数据集来有效训练和测试机器。本研究论文致力于探索并实现一种自动化方法，专门利用大型语言模型生成测试用例。该方法整合了Open AI的使用，以提升生成测试用例的效率和有效性，从而用于训练和评估大型语言模型。这种与LLM相结合的规范化方法简化了测试过程，使其更加高效且全面。借助自然语言理解能力，LLM能够智能地制定测试用例，覆盖REST API的各种属性，确保测试的全面性。研究过程中开发的模型是通过手动收集的针对各种REST API的Postman测试用例或实例进行训练的。LLM通过自动化生成多样且复杂的测试场景，增强了Postman测试用例的创建。相比传统测试用例，Postman测试用例提供了流线化的自动化、协作及动态数据处理能力，为API测试提供了一种用户友好且高效的方法。因此，开发的模型不仅符合当前技术标准，而且有望在未来技术进步中发展成为具有重要意义的理念。