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