Description: Sharing my experience of moving from manual API testing to smart, automated testing using Keploy — and integrating it into my GitHub Actions workflow.
tags: node, testing, ci, dev
From Manual Testing to Smart Automation with Keploy
Testing APIs manually is something every developer gets used to. But as the complexity of a project increases, so does the effort to maintain test cases. During my latest project, I explored how to simplify this with AI — and the results were better than I expected.
Project Overview
I built a backend project named Event Tracker API using Node.js, Express.js, and MongoDB Atlas. It supports basic CRUD operations to create, read, update, and delete events.
Initially, I tested it using Thunder Client, and later wrote unit, integration, and API tests manually with Jest and Supertest. This gave me decent test coverage, but maintaining it wasn’t scalable.
Getting Started with Keploy
Keploy allowed me to auto-generate test cases by simply interacting with my API. I didn’t have to manually define mocks or request payloads — it recorded real API calls and built test cases using them.
What stood out:
- No need to write tests manually
- Real data was captured
- Tests were deterministic and replayable
I used my OpenAPI schema and curl commands to feed context. Keploy handled the rest.
Integrating into GitHub Actions
To automate everything further, I added Keploy to my GitHub Actions pipeline.
A few things I did:
- Added
MONGO_URIas a GitHub Secret - Updated my workflow YAML to install Keploy and run tests
- Increased the server boot time to avoid connection issues
Seeing all green checks in the pipeline and a live badge in the README was satisfying.
API Testing with Chrome Extension
I also used Keploy’s Chrome extension to test live API calls from websites I frequently use. This helped me understand how dynamic data from frontends can be captured and replayed for regression testing — without having access to backend code.
Key Learnings
- AI-powered API testing is faster and more reliable than I expected.
- CI/CD integration gets easier once secrets and dependencies are handled properly.
- Keploy works well with real projects without requiring major changes.
- Keploy’s auto-recording of curl commands saved a ton of setup time.
Visual Proofs
Final Thoughts
This was my first time using AI for testing and automating it in CI/CD. It was a great learning experience — not just about tools, but about best practices in building production-ready APIs.
The full project is live on my GitHub:
👉 https://github.com/hansikachaudhary/event-tracker-api
Built with:
Node.js • Express • MongoDB Atlas • Jest • Supertest • Keploy
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