From Requests to Results

In today’s microservices-driven world, APIs form the backbone of modern applications. Writing robust API tests isn’t just good practice—it’s essential for reliability, speed, and confidence in your software delivery.

Why API Testing Matters

API testing helps you:

1. Catch issues before they hit production (Reliability)
2. Use tests as working examples (Documentation)
3. Deploy confidently with coverage (Confidence)
4. Ensure services work well together (Integration)

Setting Up Your Environment

# Create a virtual environment
python -m venv venv
source venv/bin/activate

# Install testing libraries
pip install requests pytest pytest-cov responses

Basic API Testing

# test_basic_api.py
import requests
import pytest

BASE_URL = "https://api.example.com/v1"

def test_get_user():
    response = requests.get(f"{BASE_URL}/users/1")
    assert response.status_code == 200
    data = response.json()
    assert all(key in data for key in ("id", "name", "email"))

def test_create_user():
    payload = {
        "name": "John Doe",
        "email": "john@example.com"
    }
    response = requests.post(f"{BASE_URL}/users", json=payload)
    assert response.status_code == 201
    assert response.json()["name"] == payload["name"]

Advanced Testing Techniques

1. Reusability with Fixtures

# conftest.py
import pytest
import requests

@pytest.fixture
def auth_header():
    token = "your-auth-token"
    return {"Authorization": f"Bearer {token}"}

@pytest.fixture
def test_user():
    user_data = {"name": "Test User", "email": "test@example.com"}
    response = requests.post("https://api.example.com/v1/users", json=user_data)
    user = response.json()
    yield user
    requests.delete(f"https://api.example.com/v1/users/{user['id']}")

2. Mocking API Responses

# test_mocked_api.py
import responses
import requests

@responses.activate
def test_user_not_found():
    responses.add(
        responses.GET,
        "https://api.example.com/v1/users/999",
        json={"error": "User not found"},
        status=404
    )
    response = requests.get("https://api.example.com/v1/users/999")
    assert response.status_code == 404
    assert response.json()["error"] == "User not found"

3. Parameterized Testing

# test_parameterized.py
import pytest
import requests

BASE_URL = "https://api.example.com/v1"

@pytest.mark.parametrize("user_id,expected_status", [
    (1, 200),
    (999, 404),
    ("invalid", 400)
])
def test_get_user_scenarios(user_id, expected_status):
    response = requests.get(f"{BASE_URL}/users/{user_id}")
    assert response.status_code == expected_status

Best Practices

• Isolate each test
• Always clean up test data
• Use environment variables for credentials and API URLs
• Validate both success and error responses
• Write clear and concise test descriptions

Running Tests with Coverage

pytest --cov=your_api_package tests/

Common Pitfalls

Avoid these mistakes:

• Using hard-coded credentials
• Ignoring failure scenarios
• Leaving behind test data
• Relying on fragile data order
• Making real API calls when mocking is sufficient

Next Steps

Here’s what you can explore next:

• Integrate API tests into CI/CD
• Add load testing using Locust
• Use Pact for contract testing
• Perform security testing with OWASP ZAP
• Monitor APIs in production

Good tests don’t just prevent bugs—they empower development.

Stay tuned for deeper dives into performance, contract, and security testing.