Selenium architecture is based on a client-server model where test scripts communicate with browser drivers through APIs and protocols. Here are the key points you should know: Core Components of Selenium Architecture

Selenium Client Libraries

• Available in multiple languages (Java, Python, C#, Ruby, etc.).
• Test scripts are written using these libraries to send commands to browsers.

JSON Wire Protocol / W3C WebDriver Protocol

• Acts as a bridge between client libraries and browser drivers.
• Converts commands into a standard format that browsers can understand.
• Selenium 4 fully supports the W3C WebDriver protocol, improving compatibility with modern browsers.

Browser Drivers

• Each browser has its own driver (e.g., ChromeDriver, GeckoDriver for Firefox, EdgeDriver).
• Drivers receive commands from the client via HTTP and execute them in the browser.
• They return responses back to the client libraries.

Browsers

• The actual environment where automation happens.
• Drivers directly interact with browsers to perform actions like clicking, typing, or navigating.

Flow of Execution

1. Test Script → Written in a supported language using Selenium Client Library.

2. Client Library → Sends commands via JSON Wire/W3C protocol.

3. Browser Driver → Receives commands and communicates with the browser.

4. Browser → Executes the actions and sends results back through the driver to the client.

Advantages of Selenium Architecture

- Cross-language support → Write tests in multiple programming languages. - Cross-browser compatibility → Works with Chrome, Firefox, Edge, Safari, etc. - Scalability → Selenium Grid allows parallel execution across machines. - Flexibility → Direct communication with browsers ensures faster execution compared to older Selenium RC

Why Python Virtual Environments Matter

1. Dependency Management

Different projects often require different versions of libraries. A virtual environment keeps dependencies isolated so they don’t conflict.

2. Version Control

You can run one project with Django 3.2 and another with Django 4.0 without issues.

3. Clean Workspace

Prevents cluttering your global Python installation with unnecessary packages.

4. Reproducibility

Makes it easier to share projects with others. They can recreate the same environment using requirements.txt.

Example Scenario Imagine you’re working on two projects:

Project A→ Needs Flask==2.0.0

Project B → Needs Flask==3.0.0

Without virtual environments, installing both versions globally would cause conflicts. With virtual environments:

projectA_env → install Flask 2.0.0

projectB_env→ install Flask 3.0.0

Both projects run smoothly, side by side.