2024/04/16 by Sulistyowati Sri, Sri, S Deepika, Mohammed Aadil S +9 · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Service-Oriented Architecture and Web Services #Software Engineering (cs.SE) #Software System Performance and Reliability #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2404.10678
openalex publication_date 2024/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.