Discover the Future of Visual Regression Testing in the Era of AI. Stay ahead of the curve with the latest advancements in software testing.
Regression testing ensures that modifications to the source code do not cause problems for the current system. Visual testing is centred on how the end user perceives the final interface of a regressed product. The user’s interaction with the application is significant, particularly concerning the visual elements of the web pages.
What is Visual Regression Testing?
Visual Regression Testing involves verifying the UI accuracy of an application, website, or software following any code changes.
Visual bugs significantly impact the user experience of a website, app, or software. They are highly noticeable to users and often form their initial impressions.
Moreover, Visual regression testing is equally valuable for applications, ensuring the functionality of buttons, the visibility and readability of text, and preventing ads from obstructing visual elements.
Visual Regression Testing involves generating, analyzing, and comparing browser screenshots to identify changes in pixels. These variances are referred to as visual differences, perceptual differences, CSS differences, or UI differences.
Why Visual Regression Testing?
Visual regression testing is crucial to prevent costly visual glitches from reaching the production stage. Overlooking visual validation can undermine the user experience, potentially leading to revenue loss.
How Visual Regression Testing Works?
A test runner is required to conduct visual testing and write and execute tests, along with a browser automation framework capable of replicating user actions. Numerous programs can simulate user functions, and the visual testing program captures screenshots. These screenshots are compared to the initial baseline screenshots taken before testing commenced.
Whenever a change is detected, a screenshot is captured, and some testing tools even highlight the observed differences from the baseline. Reviewers subsequently assess whether the issues stem from code-related bugs that developers need to address or if they are associated with ad integration concerns.
How do Visual Regression Tests Identify Issues?
Visual regression tests utilize screenshots to identify potential or existing issues resulting from regression. This methodology effectively pinpoints where a visual bug might have been introduced into the system.
For instance, issues could arise from overlapping text, rendering part of the page unreadable, or text crowding a button, rendering it non-functional. Visual regression testing also checks for ads covering the page, leading to unreadable text or inaccessible buttons and links. Compatibility issues across browsers, screen sizes, and operating systems can contribute to such problems. Many device-screen size-resolution-operating system-browser combinations and configurations can alter how code displays pixels.
How to Implement Visual Regression Testing?
Implementing Visual Regression testing can be approached in various ways, depending on your requirements.
Define Test Scenarios:
Generate test scenarios outlining what to capture and when to capture the screenshots.
Utilize Automated Visual Testing Tools:
Employ an automated visual testing tool to compare recently taken screenshots (post-code changes) with existing application images.
Identify UI Discrepancies:
Examine the results for UI discrepancies or issues, reporting changes in the latest product design.
Address Visual Bugs:
If visual bugs are identified, address and resolve them.
Update the screenshot, designating it as the baseline image for subsequent Visual Regression tests.
Importance of Visual Validation Testing
Visual regression testing is significant, as readability issues on a website or non-functional aspects of an app due to visual interface errors can lead users to navigate away or uninstall the app. This directly impacts revenue, as users derive no benefit from such experiences.
Optimizing the user experience is crucial for maximizing revenue. The user interface must be functional and deliver the best possible experience to maintain user engagement. A visually appealing interface, complemented by a well-designed logo reflecting brand identity, is pivotal in attracting engagement and consequently influencing ad revenue, Search Engine Optimization, and other aspects of website management.
Moreover, an exemplary user interface and experience contribute to building trust and credibility with the user base. They reinforce brand image, guide users toward desired actions, and direct attention to key page elements. The overall legibility and functionality of a clean, visually balanced website are critical to achieving these objectives.
Even minor distortions in the user interface can be inconvenient or, at worst, detrimental to business. While developers may not account for every screen size and resolution, they should ensure a consistent experience across platforms such as Apple, Android, and Windows, as well as between mobile and desktop environments.
Different Approaches to Visual Regression Testing
Let’s explore the types and processes of visual regression testing.
Visual Tests Overview:
Visual tests involve producing, analyzing, and comparing screenshots to detect pixel changes. A test runner and a framework are employed, where the former enables test creation and execution, and the latter replicates user actions during browsing. Long snapshots are taken to measure modifications against a benchmark, and once set, the QA team executes the test code. The tool generates an automatic report post-execution, allowing developers to run repairs if issues are identified.
Manual Visual Testing:
This method requires developers to manually inspect the code without using automated testing tools. While suitable for early development phases, it could be faster and more convenient for comprehensive application testing. The risk of human error is also a drawback.
Layout Comparison:
This approach compares the size and position of UI elements rather than individual pixels. Changes in size or position trigger test failures.
Pixel-by-Pixel Comparison:
This method analyzes screenshots at a pixel level, highlighting discrepancies. While comprehensive, it may flag irrelevant cases and false positives, requiring manual review.
Structural Comparison:
This approach compares the Document Object Model (DOM) structure to identify HTML markup changes, failing if present.
Visual AI Comparison:
Leveraging machine learning and AI, this method takes two images without requiring a baseline image. It mimics human vision, preventing false positives and effectively testing dynamic content.
DOM-Based Comparison:
Combining layout comparison and DOM analysis, this approach identifies structural changes in UI elements before and after modifications. However, results may be prone to flakiness, necessitating careful manual review.
Creating Dedicated Visual Regression Tests:
Dedicated visual regression tests can be created to maintain control over visual validation, balancing test writing efforts and time considerations.
Inserting Visual Checkpoints:
Visual checkpoints can be inserted using existing functional tests to validate application functionality. However, it limits test coverage tools.
Inserting Implicit Visual Validation:
By adding implicit visual validation to the existing test framework, this method incorporates visual checks with minimal additional code and is suitable for genetic validation.
Manual vs. Automated Visual Regression Testing:
While manual testing is prone to errors and time-consuming, automated visual testing offers speed, accuracy, and long-term cost efficiency. Despite higher initial expenses, automated testing proves beneficial for continuous testing and test maintenance over time. Choosing between manual and automated testing depends on speed, accuracy, and overall project requirements. Automation testing is especially advantageous for complex and dynamic applications with frequent changes.
Integrating AI in Visual Testing
The testing landscape is transforming, moving away from traditional methods reliant on manual inspection or automation frameworks like Selenium. The latter often faced challenges with evolving application code, leading to false negatives and necessitating frequent script revisions.
AI emerges as a more effective solution. Testing tools incorporate advanced “Visual Locators” that enhance robustness, eliminating issues associated with rigid selector-based approaches. AI in Visual Testing utilizes visual locators similar to human perception, mitigating challenges posed by changes in element selectors.
Utilizing Visual AI
Visual AI is extensively utilized and influences various markets and industries. Examples include Apple’s Face ID, automatic image labelling in Google Photos and cashier-free stores like Amazon Go. Visual AI advancements power technologies such as self-driving cars, medical image analysis, sophisticated image editing tools, and software visual testing for bug prevention.
Limitations of Snapshot Testing
Snapshot testing, aimed at assessing an application’s appearance, has limitations. Relying on baseline snapshots for comparisons, scrutinizing at the pixel level often results in false positives due to factors like anti-aliasing effects, dynamic content changes, and browser variability. These challenges generate dissatisfaction among QA test engineers, requiring manual intervention to address false positives.
How Visual AI Works?
Visual AI addresses the drawbacks of pixel and DOM techniques by identifying visual elements on a screen or webpage. Instead of pixel-level analysis, Visual AI uses computer vision to recognize elements as objects with various attributes, similar to human vision. It then compares checkpoint elements against baselines, detecting visible differences.
Workflow of the Visual AI Solution
Data Collection and Preprocessing:
- Collect baseline UI and DOM after a successful release.
- Capture screenshots and DOM of modified web pages when changes are applied.
- Preprocess images for comparable baseline and current images.
Element Classification:
- Examine DOM elements relevant for visual testing, identifying and filtering out irrelevant elements.
- Scrutinize baseline and test DOMs for changes in visual elements.
Visual AI Locators:
- Use computer vision to locate predefined visual components on web pages.
- Scan modified screenshots for the presence of locators.
Visual Differences:
- Compare locators and elements from baseline and modified pages to identify visual disparities.
- Generate a comprehensive report highlighting visual differences.
The Visual AI approach surpasses pixel and DOM-based tests by ignoring minor discrepancies, differentiating between visual and non-visual elements, and recognizing the permissible movement of visual elements.
How Visual AI Transforms Software Development and Testing Today
Manual testing remains prevalent in conventional software testing methodologies, even within organizations with automated frameworks. Visual AI addresses challenges by efficiently validating the entire web page and automating intricate locators and assertions. With the rise in application complexity and accelerated releases, Visual AI aids in maintaining test coverage and expediting the testing process across diverse platforms and browsers.
How Visual AI Aids in Cross-Browser Testing
Visual AI opens up possibilities to streamline and accelerate cross-browser and cross-device testing by embracing a ‘rendering’ approach across various device and browser combinations. LambdaTest, serving as an AI-powered test orchestration and execution platform, supports both manual and automated testing across 3000 operating systems and real devices. It offers AI-powred visual testing across 3000+ real desktop and mobile environments using frameworks such as Selenium, Playwright, Cypress, Storybook, Appium, and more!
Conclusion
In today’s digital era, most of the visual information we encounter is in digital formats. Whether accessed via desktops, laptops, or smartphones, individuals and businesses rely on extensive computing capabilities and access to myriad user-friendly applications.
The contemporary digital landscape, brimming with vast amounts of visual data, owes a significant part of its existence to the assistance provided by artificial intelligence. Visual AI, leveraging computer vision to interpret images in a manner akin to humans, plays a pivotal role. As digital content increasingly prioritizes visuals, the importance of AI in comprehending and managing images on an extensive scale has surged.
AI-driven test automation is not just a theoretical concept but a practical solution aligned with fundamental business requirements. It has the potential to scale effectively, setting the stage for the next generation of test automation.