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Anushaamar111
Anushaamar111

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My Keploy Testing Experience

#api #testing #ai 
---
title: "From Manual API Testing Hell to AI Paradise: My Journey with Keploy"
published: true
description: "A developer’s first-hand account of ditching repetitive test scripts for intelligent automation powered by Keploy AI."
tags: [api, testing, ai, automation, keploy, devtools, javascript, nodejs]
cover_image: https://res.cloudinary.com/practicaldev/image/fetch/s--Dvj0CBo3--/c_imagga_scale,f_auto,fl_progressive,h_420,q_auto,w_1000/https://keploy.io/_next/image?url=%2Fimages%2Fhero-img.png&w=1920&q=75
---

# From Manual API Testing Hell to AI Paradise: My Journey with Keploy

A developer’s first-hand account of ditching repetitive test scripts for intelligent automation powered by Keploy AI.

---

## The Pain of Manual API Testing

As a developer working on the **API-Fellowship Book Management System**, I was neck-deep in the same routine many of us know all too well:

```

bash
curl -X POST http://localhost:8000/api/add \
  -H "Content-Type: application/json" \
  -d '{"name":"Test Book","author":"Test Author"...}'

curl -X GET http://localhost:8000/api/books/64a7b2f1e4b0123456789abc
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The Harsh Reality:

  • 3–4 hours just to test a simple CRUD API
  • Frequent manual errors in test data
  • Tedious test maintenance after every API change
  • Creativity-draining repetition

Enter Keploy: AI-Powered Testing that Changed Everything

Everything shifted when I discovered Keploy — a tool that turns your real API traffic into test cases automatically.

Test Case 1: GitHub API in 2 Minutes

Target: github.com/microsoft/vscode
What I did:

  1. Installed the Keploy Chrome Extension
  2. Visited the VSCode GitHub page
  3. Clicked around and explored
  4. Keploy silently recorded every API call

Result:

  • 23 API calls captured
  • Categorized by endpoint and usage
  • Auto-generated test cases based on actual behavior

Test Case 2: E-Commerce Patterns on Amazon

Target: amazon.com
Findings:

  • Real-time price updates via WebSocket
  • Personalized recommendation APIs
  • Inventory checks and checkout flows
  • Secure CSRF and cookie handling

What Makes Keploy So Powerful?

1. Auto-Generated Smart Tests

Here’s what I used to write manually:


js
it('should create book', async () => {
  const res = await request(app)
    .post('/api/add')
    .send({...});
  expect(res.status).toBe(201);
});
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Here’s what Keploy generated automatically:


yaml
version: api.keploy.io/v1beta1
kind: Http
metadata:
  name: create-book-success
spec:
  request:
    method: POST
    url: /api/add
    body: |
      {
        "name": "The Art of Programming",
        "author": "John Developer",
        "price": 599
      }
  response:
    status_code: 201
    body:
      message: "Book added successfully"
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No guesswork. No tedious coding. Just plug-and-play.

2. Zero to 100% Coverage — In Minutes

Task Manual Testing Keploy AI
Basic CRUD Coverage 1–2 days 5 minutes
Edge Case Handling Day 3 Minute 10
CI/CD Integration Custom scripts 1-liner command

3. Dynamic Value Handling

Keploy automatically handles:

  • {{.timestamp}}
  • {{.mongoObjectId}}
  • {{.uuid}}
  • {{.authToken}}

It even understands:

  • Rate limits
  • Pattern-based errors
  • Dynamic payload differences
  • Session & token-based flows

The Numbers Don’t Lie

Metric Manual Testing Keploy AI Improvement
Setup Time 4 hours 5 minutes 4800% faster
Coverage ~60% 100% Full coverage
Maintenance High effort Minimal 90% effort saved
Errors Common None Fully automated
Developer Joy Low High Priceless

Real-World Results

GitHub.com

  • 23 API calls captured
  • Mixed REST + GraphQL architecture
  • Conditional headers (ETags) observed

Reddit.com

  • 31 API endpoints recorded
  • OAuth 2.0 login detected
  • WebSocket traffic captured

Why I’m Excited

Focus on Innovation

  • Less time on testing boilerplate
  • More time building features & UX
  • Easier refactoring with full test safety

Tests as Living Documentation

  • Keploy’s YAML is readable by anyone
  • No need to ask "What does this endpoint do?"

Continuous Learning from API Behavior

  • Detects anomalies
  • Highlights security flaws
  • Recommends better API patterns

CI/CD Integration 


yaml
# .github/workflows/keploy-tests.yml
- name: Run Keploy Tests
  run: keploy test --coverage --ai-insights
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Runs during every pull request and flags issues automatically.

Key Takeaways

For Developers:

  • Stop writing repetitive tests
  • Let AI be your testing copilot
  • Build better, faster, and with confidence

For Teams:

  • Standardize testing
  • Scale quality with minimal effort
  • Reduce onboarding time and context loss

Ready to Try It?

Here’s your 5-step Keploy launch plan:

  1. Install Keploy Chrome Extension
  2. Visit a live website and capture traffic
  3. Review the test cases
  4. Apply them to your backend API
  5. Integrate with CI/CD and ship confidently

Let's Talk

Have an API project or curious use case?
Want help automating your API testing?
Drop a comment below — I’d love to connect!

Bonus Resources 




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