Reducing Workload In Using AI-based API REST Test Generation

For communication between distributed services, modern software systems mainly rely on REST APIs, especially in microservices architectures. Preventing vulnerabilities and preserving system stability depend on these APIs\\\' dependability and security. Nevertheless, manual API testing is laborious, prone to mistakes, and frequently leads to insufficient test coverage. In order to automate the development and administration of REST API test cases, this article suggests an AI-Based API REST Test Generation framework. The system uses organized test sequences to perform automatic vulnerability detection, intelligently analyze API endpoints, and confirm application reachability. Automated endpoint discovery, authentication validation, parameter testing, and security vulnerability detection techniques like BOLA vulnerabilities and RBAC violations are all included in the framework. The proposed system is implemented as a web-based platform that visually represents the security posture of APIs through a step-wise roadmap. Experimental evaluation demonstrates that the automated framework significantly reduces manual workload while improving the efficiency and accuracy of API testing.