From Manual Struggles to AI-Powered Confidence: My Journey with API Testing Using Keploy

As a backend developer, I've always understood the importance of testing. However, like many others, I often ended up procrastinating or skipping detailed tests. Writing manual API tests felt like a chore: repetitive, time-consuming, and sometimes frustrating. But everything changed when I discovered Keploy.

The Pain of Manual Testing

In the early stages of building backend services, writing test cases was often neglected. Here's why:

• Repetition and boilerplate: Writing similar tests for multiple endpoints quickly becomes tedious.

• Time-consuming: Setting up mocks, crafting input/output, and managing test environments all take a lot of time.

• Error-prone: One wrong assumption or a missing edge case can significantly reduce your coverage.

• Hard to scale: As the number of endpoints increases, maintaining and extending test cases becomes more complex.

Even after spending hours writing test cases, coverage would sometimes barely reach 30%, and confidence in the codebase remained low.

First Look at Keploy: AI for Testing

I discovered Keploy during the API Fellowship Program. Its promise? Automatically generate unit, integration, and API tests using real request-response data. It sounded almost too good to be true.

So, I decided to try it—and the experience was eye-opening.

From Zero to Full Coverage in Minutes

With Keploy, I was able to:

• Automatically generate test cases from real API calls

• Consistently reproduce those tests with reliable mocks

• Achieve 70–100% coverage without manually creating every test

• Integrate tests into my CI/CD pipeline without additional effort

Instead of considering all possible edge cases and writing code for each, I could simply use my app as usual, and Keploy would capture and generate the tests for me.

What Makes Keploy Stand Out

Here are a few features that really made an impact:

• Chrome Extension: It captures API calls from a live website and instantly generates tests—no backend setup needed.

• OpenAPI + Swagger Schema Support: Makes it easy to document and validate APIs.

• Integration with CI/CD: Tests can be automatically integrated and run in the deployment pipeline.

• AI-Powered Mocks: Eliminates the need for tedious stubbing or seeding data.

Final Thoughts

Switching from manual to AI-powered testing was more than just a productivity boost—it fundamentally changed how I approach backend development. Testing is no longer an afterthought. It's now part of the development process, giving me confidence that my APIs are stable, scalable, and ready for production.

Keploy has made testing something I look forward to, not something I avoid.

If you're a developer tired of writing repetitive tests or wanting to improve your testing workflow, I highly recommend trying Keploy. The move from manual work to automation isn't just faster—it's smarter.

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