Visual AI Testing

Detailed explanation

Visual AI Testing leverages computer vision and machine learning algorithms to analyze the visual aspects of an application's UI. Unlike traditional functional testing, which focuses on verifying the correctness of underlying code and data, visual AI testing focuses on the "look and feel" of the application as perceived by the end-user. This includes verifying the placement, size, color, and overall appearance of UI elements across different devices, browsers, and operating systems.

The core principle behind visual AI testing is to establish a "baseline" or "golden image" of the UI. This baseline represents the expected visual state of the application. Subsequent tests then compare the current UI against this baseline, identifying any visual differences or deviations. The AI algorithms are trained to distinguish between legitimate changes (e.g., dynamic content updates) and actual defects (e.g., misaligned elements, overlapping text, incorrect colors).

Practical Implementation:

Implementing visual AI testing typically involves the following steps:

1. Setup and Configuration: Choose a visual AI testing tool and integrate it into your existing testing framework. Most tools offer integrations with popular CI/CD pipelines and testing libraries like Selenium, Cypress, and Playwright. This integration allows visual tests to be executed automatically as part of the build process.

2. Baseline Creation: Capture baseline images of the UI for different scenarios and configurations. This involves navigating to specific pages or states within the application and instructing the visual AI tool to capture a snapshot. It's crucial to create baselines that accurately represent the expected visual appearance of the application under various conditions.

3. Test Execution: Run the visual tests. The visual AI tool will automatically capture screenshots of the UI during test execution and compare them against the corresponding baseline images.

4. Difference Detection and Analysis: The AI algorithms analyze the captured screenshots and identify any visual differences compared to the baseline. These differences are highlighted and presented to the tester for review. The AI attempts to classify the differences as either legitimate changes or potential defects.

5. Review and Approval: Testers review the identified differences and determine whether they represent actual defects or acceptable variations. If a difference is deemed acceptable, the baseline can be updated to reflect the new expected state. If a defect is identified, it can be reported and tracked through the standard bug tracking process.

Best Practices:

• Start Early: Integrate visual AI testing early in the development lifecycle to catch visual defects as soon as possible. This prevents them from propagating to later stages and becoming more costly to fix.
• Comprehensive Coverage: Ensure that visual tests cover all critical UI elements and scenarios. This includes testing across different devices, browsers, operating systems, and screen resolutions.
• Maintain Baselines: Regularly review and update baselines to reflect legitimate changes to the UI. Outdated baselines can lead to false positives and wasted time.
• Train the AI: Some visual AI tools allow you to train the AI algorithms to better understand your application's UI. This can improve the accuracy of difference detection and reduce the number of false positives.
• Combine with Functional Testing: Visual AI testing should complement, not replace, traditional functional testing. Functional tests verify the correctness of underlying code and data, while visual AI tests ensure the UI is visually appealing and consistent.

Common Tools:

• Applitools: A popular commercial visual AI testing platform that offers a wide range of features, including automated baseline management, AI-powered difference detection, and integrations with various testing frameworks.
• Percy (BrowserStack): Another commercial visual testing platform that focuses on capturing and comparing screenshots across different browsers and devices. It integrates seamlessly with BrowserStack's cloud-based testing infrastructure.
• Happo: An open-source visual regression testing tool that uses pixel-by-pixel comparison to detect visual differences. It's a lightweight and easy-to-use option for smaller projects.
• Chromatic: A visual testing and UI review tool specifically designed for Storybook, a popular UI component development environment.

Code Example (Applitools with Selenium):

from selenium import webdriver
from applitools.selenium import Eyes
 
# Initialize the Eyes SDK and set your private API key.
eyes = Eyes()
eyes.api_key = "YOUR_APPLITOOLS_API_KEY"
 
try:
    # Open a Chrome browser.
    driver = webdriver.Chrome()
 
    # Start the test and set the browser's viewport size.
    eyes.open(driver, "My Application", "My Test", {'width': 800, 'height': 600})
 
    # Navigate to the URL.
    driver.get("https://example.com")
 
    # Visual checkpoint #1.
    eyes.check_window("Homepage")
 
    # Click a button.
    driver.find_element("id", "myButton").click()
 
    # Visual checkpoint #2.
    eyes.check_window("After Button Click")
 
    # End the test.
    eyes.close()
 
except Exception as e:
    print(e)
finally:
    # If the test was aborted before eyes.close was called, abort it now.
    eyes.abort_if_not_closed()
    driver.quit()

This example demonstrates how to use Applitools with Selenium to perform visual checkpoints. The eyes.check_window() method captures a screenshot of the current window and compares it against the baseline. Any visual differences will be reported in the Applitools dashboard.

Visual AI testing is a powerful technique for improving the quality and consistency of your application's UI. By automating the process of visual validation, it can help you catch defects that might otherwise be missed, leading to a better user experience.

Further reading

• Applitools Documentation: https://applitools.com/docs/
• BrowserStack Percy Documentation: https://www.browserstack.com/docs/percy
• Happo Documentation: https://happo.io/docs/
• Chromatic Documentation: https://www.chromatic.com/docs