From 0 to 100% API Test Coverage with Keploy AI – My Journey

Arin Kishore — Sat, 05 Jul 2025 19:19:54 +0000

Over the past few days, I had the amazing opportunity to work on API testing using Keploy AI as part of the Keploy API Fellowship. In this post, I’ll walk you through everything I did — from building a Node.js API to running AI-powered tests and integrating it into a CI/CD pipeline.

🔧 My API Project
I built a Student Manager API using:

Node.js & Express – for the backend
MongoDB Atlas – as the database
Swagger – to document the API
Endpoints for GET, POST, PUT, DELETE at /api/students

GitHub Repository:
https://github.com/kishorecodesinpython/student-api-server

🧪 Task 1 – API Testing with Keploy AI
Step 1: Created an OpenAPI Schema

I defined all endpoints and schemas using Swagger UI, hosted at /api-docs.

Step 2: Ran Keploy in Docker

Since I’m using Windows, I had to use Docker with WSL2. I ran this command: docker compose up --build
This built and launched my API and Keploy CLI together inside containers.

Step 3: Recorded API Calls

I sent multiple requests using curl and Postman to record traffic, while Keploy captured them in real-time. Then I ran: keploy test ...
This generated multiple test cases from actual traffic. I got a Test Drive report with:

27 Test Suites
20 Accepted
7 Rejected

Step 4: Debugging Docker & Environment Issues

This was not all smooth! I faced a few problems:

Docker WSL2 was broken (resolved via reset and reinstall)
MongoDB URI wasn’t passed properly (fixed using dotenv)
Curl commands needed to be corrected for schema match

Step 5: CI/CD Integration

I integrated Keploy testing into a GitHub Actions pipeline, which automatically:

Built my app using Docker
Ran all tests
Validated test outputs

🌐 Task 2 – Chrome Extension API Testing
I explored the Keploy Chrome Extension to test real-world APIs.

Site 1: DummyJSON
I captured a GET request to /products using the Chrome console and the Keploy extension.

Site 2: JSONPlaceholder
Tested endpoints like GET /posts, POST /posts, and validated response handling.

The Chrome Extension made it incredibly easy to record calls and generate test cases on the fly.

💡 What I Learned

Keploy’s AI-generated tests helped me go from zero to complete test coverage in minutes.
Docker with WSL2 on Windows takes patience and careful setup.
The Chrome extension is perfect for testing third-party/public APIs.
CI/CD test integration adds confidence to production readiness.

📸 Final Screenshots I Shared:

Swagger API Docs UI
Keploy “Test Drive” bunny report
Docker logs running Keploy
MongoDB connected confirmation in terminal

🏁 Conclusion
Thanks to Keploy, I transitioned from writing tests manually to using AI for full automation. This fellowship was one of the most hands-on testing experiences I’ve had — and I’ll definitely be applying these workflows to future projects.

GitHub Repo:
https://github.com/kishorecodesinpython/student-api-server

Let me know what you think or if you want to connect!

Keploy #APITesting #CI_CD #Nodejs #MongoDB #Docker #OpenSource #AIinTesting #KeployFellowship

DEV Community: Arin Kishore

From 0 to 100% API Test Coverage with Keploy AI – My Journey

Keploy #APITesting #CI_CD #Nodejs #MongoDB #Docker #OpenSource #AIinTesting #KeployFellowship