  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.
