Visual Comparisons

Visual Comparisons involve automatically comparing images of an application's UI to detect visual regressions or differences. This ensures the UI renders correctly across different environments and changes.

Detailed explanation

Visual comparison, also known as visual regression testing or visual UI testing, is a crucial aspect of software testing that focuses on identifying unintended visual changes in an application's user interface (UI). These changes, often subtle, can negatively impact the user experience and brand perception. Visual comparison testing automates the process of comparing screenshots or renderings of the UI against a baseline or "golden image" to detect any discrepancies.

Why is Visual Comparison Important?

Traditional functional testing often overlooks visual defects. While functional tests verify that the application behaves as expected from a logical standpoint, they may not catch issues like misaligned elements, incorrect colors, overlapping text, or broken layouts. These visual inconsistencies can arise from various factors, including:

  • Code changes: Even seemingly minor code modifications can inadvertently affect the UI's appearance.
  • Browser updates: Different browsers render web pages differently, and updates to browser engines can introduce visual variations.
  • Operating system updates: Similar to browsers, operating system updates can also impact UI rendering.
  • Responsive design issues: Ensuring that the UI adapts correctly to different screen sizes and devices is a complex task, and visual comparison helps identify responsive design flaws.
  • CSS changes: Cascading Style Sheets (CSS) control the visual presentation of web pages, and changes to CSS can have widespread effects on the UI.
  • A/B testing: When running A/B tests, it's crucial to ensure that the different versions of the UI are visually consistent except for the intended variations.

How Visual Comparison Works

The basic process of visual comparison involves the following steps:

  1. Capture Baseline Images: A set of baseline images, also known as golden images or reference images, is created for each UI element or page that needs to be tested. These images represent the expected visual state of the UI.
  2. Capture Test Images: After making changes to the application, a new set of test images is captured for the same UI elements or pages.
  3. Image Comparison: The test images are compared against the baseline images using image comparison algorithms. These algorithms analyze the pixel-by-pixel differences between the images.
  4. Difference Detection: The comparison process identifies any differences between the test and baseline images. These differences are often highlighted visually, making it easier to identify the visual regressions.
  5. Analysis and Reporting: The detected differences are analyzed to determine whether they are legitimate changes or actual defects. A report is generated, summarizing the findings and providing details about the visual regressions.

Practical Implementation and Tools

Several tools and libraries are available for implementing visual comparison testing. Some popular options include:

  • Applitools: A commercial visual testing platform that provides advanced image comparison algorithms, AI-powered analysis, and integrations with various testing frameworks.
  • Percy: A visual review platform specifically designed for web applications. It integrates with GitHub and other CI/CD tools to automate visual testing.
  • BackstopJS: An open-source visual regression testing tool that can be used to compare screenshots across different environments and screen sizes.
  • Selenium with Image Comparison Libraries: Selenium, a popular web automation framework, can be combined with image comparison libraries like OpenCV or ImageMagick to implement custom visual comparison tests.

Example using BackstopJS:

  1. Installation: npm install -g backstopjs
  2. Initialization: backstop init
  3. Configuration (backstop.json):
{
  "id": "my-project",
  "viewports": [
    {
      "label": "desktop",
      "width": 1920,
      "height": 1080
    }
  ],
  "scenarios": [
    {
      "label": "Homepage",
      "url": "http://localhost:3000",
      "selectors": [
        ".main-content"
      ],
      "misMatchThreshold": 0.1,
      "requireSameDimensions": false
    }
  ],
  "paths": {
    "bitmaps_reference": "backstop_data/bitmaps_reference",
    "bitmaps_test": "backstop_data/bitmaps_test",
    "html_report": "backstop_data/html_report",
    "ci_report": "backstop_data/ci_report"
  },
  "report": ["browser"],
  "engine": "puppeteer",
  "engineOptions": {
    "args": ["--no-sandbox"]
  },
  "asyncCaptureLimit": 5,
  "asyncCompareLimit": 50,
  "debug": false,
  "debugWindow": false
}
  1. Capture Baseline: backstop reference
  2. Run Test: backstop test

This configuration defines a scenario to capture the ".main-content" section of the homepage at "http://localhost:3000" and compares it against the baseline. misMatchThreshold sets the acceptable difference percentage. requireSameDimensions can be set to false if the dimensions are expected to change.

Best Practices for Visual Comparison

  • Establish a Baseline: Create a stable and reliable baseline of images that represent the expected visual state of the UI.
  • Automate the Process: Integrate visual comparison into your CI/CD pipeline to automate the testing process and ensure that visual regressions are detected early.
  • Use Appropriate Thresholds: Configure the image comparison algorithms with appropriate thresholds to minimize false positives and false negatives. The misMatchThreshold in BackstopJS is an example of this.
  • Focus on Critical Areas: Prioritize testing of critical UI elements and pages that are most likely to be affected by changes.
  • Handle Dynamic Content: Implement strategies for handling dynamic content, such as dates, timestamps, and user-specific data, to avoid false positives. Consider using masking techniques to ignore these regions during comparison.
  • Regularly Update Baselines: As the application evolves, update the baseline images to reflect the intended visual changes.
  • Review Differences Carefully: Carefully review the detected differences to determine whether they are legitimate changes or actual defects.
  • Consider Different Viewports: Test the UI across different screen sizes and devices to ensure responsive design.
  • Integrate with Existing Testing Frameworks: Integrate visual comparison with your existing testing frameworks to create a comprehensive testing strategy.

Challenges of Visual Comparison

  • False Positives: Minor variations in rendering can sometimes trigger false positives, requiring manual review.
  • Dynamic Content: Handling dynamic content can be challenging, as it can lead to frequent false positives.
  • Performance: Image comparison can be computationally expensive, especially for large images.
  • Maintenance: Maintaining the baseline images can be time-consuming, especially as the application evolves.

Despite these challenges, visual comparison is an invaluable tool for ensuring the visual quality and consistency of software applications. By automating the process of detecting visual regressions, it helps to improve the user experience and reduce the risk of visual defects.

Further reading

Related Terms

A/B Testing

Acceptance Testing

Accessibility Tester