In modern backend ecosystems, ensuring the reliability and security of APIs isn’t optional — it’s critical. As a QA Engineer transitioning from Customer Experience and Support, I recently completed an automated API testing project for the Gradific Learning Platform.
This project involved building a robust Postman + Newman test automation workflow with validations, error handling, performance checks, and structured reporting.
Here is how I approached it 👇
🎯 Project Goals
✅ Automate CRUD API testing
✅ Validate security and error responses
✅ Enable dynamic test data generation
✅ Reuse variables for maintainability
✅ Generate execution reports via CLI
✅ Create bug logs + execution documentation
APIs Tested:
• ✅ Authentication
• ✅ Workspaces
• ✅ Tracks
• ✅ Assignments
🛠 Tools & Technologies Used
Tool: Postman
Purpose: Build & execute API test collection
Tool: Faker.js (built-in)
Purpose: Generate dynamic payloads
Tool: Newman
Purpose: Command-line automation
Tool: HTML/JSON Newman reports
Purpose: Execution documentation
Tool: Google Sheets
Purpose: Bug reporting
Key Testing Techniques Applied
Testing Category: Positive & Negative Scenarios
Example: Valid vs invalid tokens
Testing Category: CRUD Testing
Example: POST, GET, PATCH/PUT, DELETE
Testing Category: Security Testing
Example: Unauthorized access
Testing Category: Performance Testing
Example: Response time < 500ms
Testing Category: Data Integrity
Example: ID reuse across calls
Testing Category: Script assertions
Example: JS-based validation
Final Deliverables Included
📌 Automated Test Collection (Postman)
📌 Newman HTML + JSON Reports
📌 Full Bug Report & Execution Logs
📌 Professional documentation for handoff
This is a complete end-to-end API QA workflow
Key Learnings
🔹 API behavior can differ from docs → validation is essential
🔹 Performance matters, not just correctness
🔹 Dynamic test data prevents false positives
🔹 Automation unlocks scalability and confidence
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