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Mohammad Waseem
Mohammad Waseem

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Overcoming Geo-Restrictions in Automated Feature Testing with Python and Open Source Tools

#python #qa #automation

In today's globally distributed digital landscape, testing geo-restricted or geo-blocked features poses unique challenges for QA teams. Ensuring that content behaves correctly across different regions, especially when certain features are limited or censored based on user location, requires innovative solutions. As a Lead QA Engineer, leveraging Python with open source tools provides a robust, scalable, and maintainable way to simulate diverse geographic environments for comprehensive testing.

The Challenge of Testing Geo-Blocked Features

Geo-restrictions are implemented through IP blocking, geolocation services, or third-party APIs that identify user location based on IP addresses. Testing these restrictions manually involves physically changing locations or using VPNs, which is inefficient and error-prone.

Automation demands a method to programmatically simulate requests from various geographic regions without excessive complexity or cost.

Solution Overview: Mimicking Geolocation with Python

Using Python, we can manipulate request origins and geolocation data seamlessly. The key components include:

  • Proxy Servers to route requests through different locations
  • Geolocation Databases or APIs for IP-to-location mapping
  • Open source libraries like requests, httpx, along with proxy tools like mitmproxy or rotating proxies

Implementing Geo Simulation with Python

Step 1: Set Up Proxy Servers or Services

First, subscribe or set up open source proxies. For example, you can use free proxy lists available online, or deploy your own proxy servers.

Step 2: Use Python to Rotate IPs and Modify Requests

Here's an example of how to request different geo-locations using requests with proxy support:

import requests

# List of proxies from open sources (sample)
proxies = [
    {'http': 'http://proxy1:port', 'https': 'http://proxy1:port'},
    {'http': 'http://proxy2:port', 'https': 'http://proxy2:port'}
]

# Target URL for testing
test_url = 'https://yourdomain.com/feature'

# Iterate with different proxies
for proxy in proxies:
    try:
        response = requests.get(test_url, proxies=proxy, timeout=10)
        print(f"Proxy: {proxy['http']} - Status: {response.status_code}")
        # Additional validation logic here
    except requests.RequestException as e:
        print(f"Error with proxy {proxy['http']}: {e}")
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Step 3: Automate Geolocation IP Mapping

To dynamically assign IPs based on geographic regions, integrate open source IP geolocation datasets like MaxMind's GeoLite2. Use the geoip2 library to resolve IP addresses:

import geoip2.database

# Initialize GeoIP reader
reader = geoip2.database.Reader('./GeoLite2-City.mmdb')

# Sample IPs or proxies
ips = ['YOUR_IPS_HERE']

for ip in ips:
    try:
        response = reader.city(ip)
        country = response.country.name
        city = response.city.name
        print(f"IP: {ip} - Location: {city}, {country}")
        # Use location info to select appropriate proxy or request parameters
    except Exception as e:
        print(f"Error resolving IP {ip}: {e}")
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Integrating with Your Test Suite

Combine these tools into your testing pipeline using frameworks like pytest or Robot Framework. Write parameterized tests to run features under different geo-simulated scenarios, verifying content access, feature availability, and UI behavior.

Key Considerations

  • Proxy reliability: Regularly update your proxy list for coverage and stability.
  • Accuracy: Geolocation databases may have discrepancies; cross-verify with large datasets.
  • Compliance: Ensure you adhere to terms of service for proxy use and geolocation data.

Final Thoughts

By effectively leveraging open source tools and Python scripting, QA teams can simulate diverse geographic conditions, ensuring that geo-restriction features are thoroughly validated before deployment. This approach not only improves test coverage but also streamlines workflows, reduces reliance on manual steps, and enhances confidence in regional compliance.

In an era where digital products serve a global audience, robust geo-testing is essential. Open source Python solutions offer the flexibility and power needed for modern QA challenges, making them indispensable tools in your testing arsenal.

🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.

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