This research demonstrates how Large Language Models can automatically enhance REST API test suites through test amplification, both as single agents and in multi-agent systems.

• Enhanced test coverage by exploring boundary conditions and edge cases
• Automated test generation that requires minimal human intervention
• Multi-agent LLM systems provide superior results compared to single-agent approaches
• Improved bug detection for more robust and secure APIs

For engineering teams, this represents a significant shift in API quality assurance, potentially reducing manual testing effort while improving API security and reliability through automated, intelligent test amplification.

Test Amplification for REST APIs via Single and Multi-Agent LLM Systems