This research explores how Large Language Models can be used to amplify and improve automated testing for REST APIs without requiring specialized fine-tuning.

• Demonstrates how "out-of-the-box" LLMs can generate more robust test cases that target boundary conditions
• Shows practical applications of ChatGPT and GitHub Copilot in test automation workflows
• Provides evidence that LLMs can enhance test coverage for critical cloud-native applications

As engineering teams build increasingly complex cloud services, this approach offers a promising path to improve test quality and security without significant additional engineering effort.

Test Amplification for REST APIs Using "Out-of-the-box" Large Language Models