This content originally appeared on DEV Community and was authored by Krish Nayyar
As a backend developer working on microservices, I've always understood the importance of testing — but actually writing and maintaining those tests? That was a whole different challenge.
The Struggle with Manual API Testing
Before I started using Keploy, my API testing workflow was completely manual. Every time I made changes to routes, logic, or validations, I had to:
Reopen Postman or Swagger
Send requests one by one
Manually write tests using tools like Jest or Supertest
Mock external dependencies and write assertions from scratch
This approach was time-consuming, repetitive, and, to be honest, pretty boring after the 10th similar test case. Plus, ensuring full test coverage across all endpoints — with all variations of edge cases — felt nearly impossible in a fast-paced development cycle.
Enter Keploy: AI-Generated API Tests
Then I discovered Keploy — a testing tool that uses AI to auto-generate tests based on real traffic. At first, I was skeptical. Could a tool really generate meaningful tests automatically?
Turns out… it can. And it did.
Once I integrated Keploy into my microservice (movie_service), I ran the application normally and let Keploy record my API traffic. Every request I sent through Swagger or even the frontend was captured, converted into test cases, and stored automatically.
The best part? I didn’t have to write a single line of test code.
From 0% to 100% Coverage in Minutes
In a matter of minutes, Keploy had captured and replayed real-world scenarios across multiple endpoints — including edge cases like missing query params and invalid request bodies.
All the recorded tests could be:
Replayed against future versions of the app
Validated automatically
Used as regression tests — without me lifting a finger
Keploy didn’t just save time — it elevated the reliability of my APIs with zero additional effort.
First Impressions of AI Testing
At first, it felt almost too good to be true. I’ve always seen testing as something I had to "grind through" — but Keploy turned it into a byproduct of normal usage. That’s a huge mental shift. The Chrome extension, in particular, helped me capture requests from tools like Swagger and turn them into tests effortlessly, leveraging AI to analyze payloads and responses intelligently.
What Excites Me About the Future
Moving from manual tests to AI-driven automation excites me for a few reasons:
Speed: I can focus on building, not writing tests.
Confidence: Keploy catches regression issues instantly.
Scalability: As I build more services, I know my test coverage won’t fall behind.
Dev-Experience: It integrates smoothly into my CI/CD workflows using GitHub Actions.
With tools like Keploy, I can stop dreading testing — and start embracing it as an automated safety net.
This content originally appeared on DEV Community and was authored by Krish Nayyar
Krish Nayyar | Sciencx (2025-06-27T22:27:58+00:00) From Manual API Testing to AI-Generated Coverage with Keploy. Retrieved from https://www.scien.cx/2025/06/27/from-manual-api-testing-to-ai-generated-coverage-with-keploy/
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