What if your tests could write themselves? Here's how mine almost did.
During a recent project, I built a full-stack app with a set of RESTful APIs — and like any good practice, I knew testing was essential. So, I began writing tests manually.
Sounds straightforward? It wasn't.😅
The Manual API Testing Struggle
I found myself constantly juggling between writing business logic, managing routes, setting up sample payloads, and then recreating all of that again just to test it.
Writing unit tests for each endpoint, mocking database calls, and manually creating edge-case scenarios took as much time — sometimes more — than building the features themselves.
Even with everything set up, maintaining these tests became another full-time task:
- 🔁Change one field in the response? All the tests break.
- 🧪Add a new validation? Go back and write test cases again.
- 🔧Refactor an endpoint? Hope you remember to update the mocks too.
It was exhausting. 😓
The Turning Point: Discovering Keploy
While searching for smarter solutions, I came across Keploy, an AI-powered API testing toolkit. The concept was compelling — instead of writing every test manually, Keploy captures API traffic and auto-generates test suites based on real requests and responses.
It took just minutes to integrate. And suddenly, I was spending time on features, not fighting failing test cases.
Here’s what I loved:
- ✅ No need to write mock data or seed databases.
- ✅ Captures real traffic and turns it into deterministic test cases.
- ✅ Keeps up with changes and helps reduce flaky tests.
- ✅ CLI + GitHub Actions support made CI/CD testing seamless.
And now, with Keploy's automation:
Keploy Chrome Extension (Bonus 🚀)
To further explore, I tried out the Keploy Chrome Extension on live websites that fetch data via API calls. With just a few clicks 🖱️, I could record and replay requests, inspect headers and payloads 📦, and generate tests ✅ directly from real interactions.
I tested on:
- [AccuWeather] – captured weather API usage
- [YouTube] – tested search/filter requests
This was super helpful in understanding how frontend interacts with APIs, and how automated test generation works across any endpoint.
Seamless CI/CD Integration
After generating my test suites with Keploy, the final piece of the puzzle was making them run automatically during each deployment. And that meant — yes — diving into CI/CD.
I’ll be honest: I expected a headache. Setting up GitHub Actions often means YAML acrobatics 🤸, cryptic error logs 🧾, and lots of trial and error. But with Keploy, it was surprisingly smooth.
In every push or PR:
My API spun up in the GitHub runner 🚦
Keploy connected, pulled test cases from the dashboard 📥
It replayed real API calls with captured payloads — no mocks, no fake data 🧪
Final Thoughts: Is AI Testing the Future?
Manual testing teaches you a lot — edge cases, validation handling, mocking — but it doesn’t scale well.
Keploy feels like the missing piece in the modern dev workflow. It allows you to focus on writing real code while ensuring your APIs remain testable, robust, and well-documented.
If you're tired of chasing broken mocks or rewriting the same tests every week, give Keploy a shot.
✍️ Thanks for reading. If you found this post insightful, let me know in the comments or connect on GitHub!