A Complete Guide to Visual Regression Testing

Visual regression testing is a critical technique that complements functional testing to deliver high-quality software faster. As per Gartner, over 50% of mid-to-large size companies will be using visual AI-based testing solutions by 2025. In this comprehensive guide, we will explore what visual regression testing is, why it matters, best practices to implement it, top tools, and more.

What is Visual Regression Testing?

Visual regression testing checks for unintended changes to the look and feel of web or mobile applications during development. It works by comparing current screenshots of the application against approved baseline screenshots after code changes.

Unlike regular regression testing which focuses on functionality, visual regression testing catches visual bugs – issues that affect the visual presentation of the application. For example, UI elements appearing cut-off on certain viewports, colors or fonts changing unexpectedly, responsiveness issues, etc.

How Visual Regression Testing Works

The workflow is as follows:

1. Approved baseline screenshots are captured for critical flows when the application is working as intended visually. These act as the "source of truth" for comparison.

2. As developers modify code, new screenshots are automatically captured across various flows, viewports and devices.

3. Visual testing tools compare the new screenshots against baseline images using intelligent algorithms. Any pixel-level differences, known as visual diffs, are highlighted.

4. A human reviewer analyzes the diffs to see if they represent a real bug or an intended change. Real bugs are logged and fixed by developers.

5. Once fixed, the reviewer approves the new screenshots as updated baselines for future tests.

This way visual issues can be detected early, even before code is merged downstream. Over time, visual test coverage is systematically expanded across the application.

Benefits of Visual Regression Testing

Here are some of the top benefits of incorporating visual regression testing:

1. Catch Visual Bugs Early

Studies show that fixing bugs later in development can be 100x more expensive. Visual testing shifts bug detection leftwards, eliminating a whole class of defects before they impact end-users.

2. Ensure UI Consistency Across Devices

By testing across real mobile devices, browsers and viewports in parallel, teams can ensure a consistent visual experience for their diverse customer base.

3. Accelerate Release Velocity

Engineering teams can merge PRs faster knowing that any visual changes will be caught automatically. This results in more frequent, high-quality releases.

4. Enhance User Experience

Delivering flawless UI/UX leaves customers delighted, driving up retention and referrals for the business.

5. Save Time and Money

Automating visual checks eliminates hours of monotonous and error-prone manual screenshot reviews. And it reduces downstream support costs due to visual bugs reported by users.

How to Implement Visual Regression Testing

Here is a step-by-step guide to implementing automated visual regression testing:

1. Define Critical User Journeys

Decide which flows you want to visually test first – for example, login, checkout, homepage UI. Expand to other flows over time.

2. Configure Devices, Viewports and Browsers

Catalog the combinations you need to test to represent your actual user traffic distribution. Popular choices are latest iOS/Android devices, and desktop/mobile viewports.

3. Select a Visual Testing Tool

Leading options include Percy, Applitools, Chromatic and BackstopJS. We discuss the top 10 tools later in this post.

4. Start Snapshotting

Use the tool‘s SDK or capabilities to insert snapshot capture commands within your test scripts at appropriate points. Execute the scripts to create approved baseline screenshots.

5. Integrate into CI/CD

Configure your CI/CD system (GitHub Actions, CircleCI etc.) to run visual regression tests automatically for every code change.

6. Review and Resolve Diffs

Analyze any flagged differences between new and baseline screenshots, and log actual bugs. Have developers fix them.

7. Update Baselines

For diffs that represent intended UI changes, approve the new screenshots as updated baselines for future comparison.

This creates a safety net ensuring the application‘s visual presentation never unexpectedly deteriorates as development progresses. Teams gain the confidence to release changes faster.

Top 10 Open Source Tools for Automated Visual Testing

Here are the 10 most popular open source tools for visual regression testing:

1. Percy

Percy is an end-to-end platform purpose-built for visual testing. It offers advanced computer vision algorithms to detect minute visual differences accurately. Teams can run Percy on their own infrastructure or via Percy‘s cloud-based service.

2. Applitools

Applitools uses AI and machine learning for smart image analysis. It also provides interactive visual debugging features within its browser-based dashboard.

3. Screenster

Screenster is a lightweight option that integrates well with popular test runners like Selenium, Cypress and Playwright. It can auto-group and analyze screenshots during parallel test execution.

4. Chromatic

Chromatic is targeted specifically at UI component libraries and documentation sites. It integrates tightly with Storybook and can help track visual changes for each component version.

5. DiffBitmap

As the name suggests, DiffBitmap compares bitmap images to find pixels that have changed. It generates detailed graphical diff reports for easy bug diagnosis.

6. BackstopJS

BackstopJS offers capabilities like bulk screenshot comparison, smart cropping of differences and support for dynamically generated pages. It also lets teams set custom thresholds for allowed level of variance.

7. Resemble.js

Resemble.js performs pixel analysis for images using Canvas and JavaScript. It is straightforward to use and offers configurable tolerance levels while comparing images.

8. PhantomCSS

PhantomCSS stems from the PhantomJS testing framework. It captures screenshots using regular CSS selectors and asserts for visual regressions.

9. Gemini

Gemini supports testing web and native mobile apps. It can integrate with CI systems and even auto-raise GitHub issues when differences are found.

10. Wraith

Wraith lets users customize test contexts and scenarios. It also saves image processing time via smart asynchronous screenshot comparison.

When evaluating tools, assess smart features like flake rate reduction, configurable ignored regions, threshold customization and interactive visual diffs. Percy and Applitools lead the pack when it comes to advanced capabilities.

Best Practices for Visual Regression Testing

Here are some proven real-world best practices for unlocking the full benefits of visual testing:

Start Early

Begin visual regression testing as early as possible, ideally from the second sprint. This prevents inheriting visual debt down the line.

Prioritize Critical User Flows

The top processes impacting customer conversion and retention should be covered first before expanding to secondary flows.

Gradually Increase Coverage

While 100% visual coverage may not be necessary, teams should keep augmenting tests across different application areas over time.

Integrate With Mainline Development

Enable developers to run visual tests locally before checking in code changes to nip bugs in the bud.

Review Diffs Rapidly

Have testers periodically review diffs and baseline updates to ensure tests don‘t stale and effectiveness remains high.

Test Across Devices and Viewports

Verify application appearance under different environments that reflect real-user preferences and settings.

Common Challenges and Mitigations

Here are typical pain points teams face when implementing visual regression testing, along with proven mitigations:

Flaky Baseline Tests

Use tools with advanced algorithms, image preprocessing and built-in wait mechanisms to minimize test flakiness.

Too Many False Positives

Configure region masking, custom acceptable thresholds and ignored change filters to reduce false positives.

High Test Maintenance Overhead

Opt for dynamic scripting and parameterized test creation instead of recording absolute values in scripts.

Lack of Real Mobile and Browser Coverage

Leverage remote browser and device testing platforms like BrowserStack and Sauce Labs to access thousands of environment combinations instantly.

Conclusion

Automated visual testing is pivotal for CI/CD and cross-browser testing strategies in the modern software delivery landscape. When implemented correctly, visual regression testing can accelerate release velocity by 2x-3x, reduce visual bugs by over 50%, and delight end-users with flawless UIs.

This guide covers everything teams need to successfully incorporate visual regression testing – best practices, primary challenges and mitigations, choosing the right tools, and building a solid foundation for innovation velocity. The time to embrace visual testing is now.