How I Went From 0 to 100% API Test Coverage in Minutes with Keploy

Ali Rizvi — Sat, 28 Jun 2025 01:49:52 +0000

🧪 From Manual to Magical: My Experience with AI-Powered API Testing Using Keploy

💭 The Problem: Manual Testing Woes

As a student developer working on a Flask + MongoDB API project called StudySync, I started off testing the usual way—writing unit and integration tests manually. It was okay… until it wasn’t. Every API change meant updating tests, mocking the database, and praying the test coverage didn’t drop like my phone’s battery at 2%.

🧠 Enter Keploy: AI to the Rescue

When I was introduced to Keploy, an open-source API testing tool powered by AI, I was intrigued. Could it really record my real API usage and generate tests automatically?

Spoiler alert: YES. It felt like going from chopping veggies with a blunt knife to using a laser cutter.

🚀 My Setup

I was working on a Flask app with MongoDB. Here’s a quick view of what I had:

A REST API with endpoints like /topics (GET, POST, PUT, DELETE)
A simple frontend with HTML + JS
Tests written manually using unittest
Then came Keploy…

🔍 Using Keploy: The Experience

1. Recording with Keploy

Keploy let me wrap the python app.py command and record real HTTP requests as I interacted with my app (using Postman or browser).

./keploy record -c "python app.py" --record-timer 1m

Within a minute, it had captured all the API calls I made—GET, POST, even MongoDB operations—and stored them as test cases.

2. Running the Tests

I then ran:

./keploy test

It replayed the API calls as tests—mocking the database and external dependencies—without writing a single new test function. I saw real test output, coverage info, and differences where my app behavior changed.

✅ Results

Test Type	Before Keploy	After Keploy
Manual Unit Tests	✅ Tedious, partial coverage	❌ Not scalable
Keploy AI Tests	🚫 Non-existent	✅ 90%+ coverage in 1 minute
MongoDB Mocks	🧱 Had to write manually	🧠 Done automatically

💥 Highlights & First Impressions

🧠 Zero Code Test Generation – Keploy created meaningful tests based on how I actually use my app.
⚡ Fast Feedback Loop – I went from 0 to 100% test coverage in under a minute.
🧪 Mocks MongoDB Out of the Box – No more writing fake database calls.
🌐 CI/CD Ready – You can plug this into GitHub Actions easily.

🧠 Lessons Learned

AI can genuinely save hours of boring test writing.
Tools like Keploy make testing accessible even for student developers like me.
Having test cases recorded from real behavior ensures you’re testing what actually matters.

🧪 What’s Next?

Integrating Keploy into my CI pipeline
Exploring more use cases with the Keploy Chrome Extension
Replacing even more manual tests with AI-powered ones

🔗 Useful Links

📦 My GitHub Repo: github.com/AliRizvi433/studysync-api
🧠 Keploy Docs: https://keploy.io/docs
🌐 Chrome Extension Guide: Keploy Extension

🙌 Final Thoughts

Keploy helped me spend less time testing and more time building. If you’re a developer looking to speed up your testing process and love automation, Keploy is worth a try.

DEV Community: Ali Rizvi