From Manual to Magic: My First Experience with Keploy’s Chrome Extension for API Testing



This content originally appeared on DEV Community and was authored by MD Sabith

As a budding developer constantly searching for ways to make my workflow more efficient, I recently stumbled upon something that fundamentally changed the way I look at API testing — the Keploy Chrome Extension.

Before diving into what made Keploy a game-changer, let me paint a picture of my life before automation and how AI-powered testing helped me achieve in minutes what would usually take hours.

👨‍💻 Life Before Keploy: The Manual Testing Maze
In the early days of building APIs, testing felt like a side quest — something I knew I had to do but never enjoyed. Here’s what manual API testing looked like:

Open Postman or Insomnia.

Manually set up each request: endpoint, headers, body.

Click “Send”, wait for a response.

Save the response (maybe).

Change some values and repeat the process.

And then came the real pain: Writing proper test cases. I had to think through each condition, edge case, and validation manually. Every bug I caught felt more like luck than strategy.
Enter Keploy: API Testing, Reinvented
When I discovered the Keploy Chrome Extension, my curiosity was piqued. It promised automatic test generation from real API calls and instant test coverage — all with a click. I had nothing to lose, so I gave it a shot.

Spoiler alert: I was blown away.

🔧 Getting Started with Keploy Chrome Extension
The process of setting up and using the extension was straightforward:

Installed the Keploy Chrome Extension.

Chose two API-rich websites to test:

https://reqres.in: A mock user data API service.

https://jsonplaceholder.typicode.com: A fake REST API for prototyping.

Opened Chrome DevTools, navigated to the Network tab, and then activated the Keploy extension.

Clicked around the site to trigger API calls — such as:

Fetching users.

Submitting POST requests.

Deleting or updating fake records.

As I interacted with the pages, the extension quietly did its job: capturing every API call and its metadata.

The Magic Moment: Auto-Generated Test Cases
Once I stopped the recording, Keploy presented me with a clean list of API calls it had tracked — and allowed me to export them as test cases with a single click.

It was surreal.

No JSON editing, no formulating requests. Keploy had built a full test suite from my real-world browsing session.

Key Takeaways and Learnings
🔍 1. Shift-Left Testing Made Easy
Keploy supports developers to bring testing earlier into the development process. As I was coding, I could run the app and generate tests on the fly — without ever opening Postman or writing test scripts manually.

⚡ 2. 100% Test Coverage in Minutes
With traditional tools, covering 100% of endpoints is near impossible without deep planning and huge time investment. Keploy captured all the APIs I used during normal browsing, giving me test coverage instantly.

🔄 3. Perfect for Regression Testing
One powerful use case is regression testing. Once tests are generated, I can re-run them whenever the API or backend changes to ensure nothing breaks.

😓 Challenges I Faced
No tool is perfect, and my first run with Keploy wasn’t either:

Initial Confusion: I wasn’t sure how to trigger the recording. A quick tutorial video helped.

Edge Case Coverage: Since Keploy captures based on real interaction, I had to manually simulate edge cases (e.g., missing fields) to get those test cases captured.

Understanding Test Output: It took a few minutes to get familiar with the test format.

But all of these were learning curves, not blockers.

My Thoughts on AI-Driven Testing:
I’m a developer, not a full-time QA engineer. My goal is to build and ship software quickly, without compromising on quality. Keploy feels like it was built exactly for people like me.

Here’s why I believe AI-driven testing is the future:

✅ It reduces the friction of writing tests.

🚀 It fits perfectly into CI/CD pipelines.

🧠 It lets me think about behavior instead of test syntax.

💡 It enables non-testers (like interns or junior devs) to contribute to QA meaningfully.


This content originally appeared on DEV Community and was authored by MD Sabith