The Power of Visual Regression Testing: Why It Is Critical for Enterprise Apps

 Visual testing plays a vital role in ensuring that software products meet the aesthetic requirements specified by clients in functional and non-functional specifications. In today's highly competitive digital landscape, users expect a seamless and visually appealing online experience. Visual testing, also known as automated UI testing or visual regression testing, verifies the accuracy of the visual elements that users interact with. By incorporating visual testing into the software verification process, enterprises can ensure that their products meet user expectations and avoid the negative consequences of a poorly designed user interface.

As per Gartner's forecast, by 2026, visual AI-powered scanning tools are expected to identify 80-90% of detectable accessibility issues, which marks a substantial improvement from the current rate of 40-60%.

This blog explores the importance of visual testing and why it has become an essential component of enterprise app development in recent years.

Why SREs Must Prioritize Visual Testing

Visual testing is a critical aspect of software development as an application's UI significantly impacts its success in the market. Many major companies, such as Google, Adobe, Yahoo, and Twitter, recognize the importance of automated visual testing and have dedicated teams for this purpose. 

Here are some reasons why visual testing is vital:

• A poorly designed UI can lead to customer inconvenience and negatively impact a company's reputation.
• Difficulty in using an application on different browsers can lead to the loss of clients and revenue.
• With the availability of numerous operating systems and devices with varying screen sizes, applications need to be responsive to run seamlessly across different platforms.
• A customer's first impression of a software product is based on its UI and ease of use. A negative experience with the UI can discourage customers from using the application, even if its functionality is proficiently working, leading to a waste of investment in the software.

Learn more: Top 5 Data-Driven Reasons to Use Audio Visual AI for End-to-End Testing.

How Developers Should Utilize Visual Testing and Its Techniques

The visual testing method involves generating, analyzing, and comparing browser snapshots to detect any changes in pixel values, known as visual diffs. To conduct visual testing, Quality Assurance (QA) requires a test runner to write and execute tests and a browser automation framework to mimic user actions. Developers create code to replicate user functions and capture screenshots at relevant points. The first set of screenshots serves as a baseline for future comparisons. During test runs, any changes trigger a screenshot capture and comparison against the corresponding baseline image. A test fails if discrepancies arise. The test generates an automatic report that the QA reviews, including any differences from the baseline. Developers can fix and rerun the test if discrepancies are due to bugs. If the differences arise from changes in the user interface (UI), developers review the screenshots and update the baseline images for future visual testing.

Visual testing is a critical process in the software testing cycle, and it can be performed either manually or through automated tools. Manual visual testing requires manual testers to spot differences in application UI using snapshots or manuals provided by developers. This process is time-consuming and needs to be able to detect minor UI changes accurately. Automated visual testing, on the other hand, allows for speedy and concurrent testing across various screens and browsers. It checks the positioning and correctness of components and factors like overlapping text, hiding elements, color, size, and shape. Several tools are available for automated visual testing, including Selenium, Watir, WebdriverIO, and Storybook. However, the automated process requires high maintenance of baseline tests for expected images for various screens, resolutions, operating systems, and browsers. Balancing automated and manual testing is crucial to ensure effective results without unnecessary bugs or test failures.

Read: Streamlining QoE Testing - Harnessing Automation for Media Streaming Platforms

How Automated Visual Testing Is Gaining Traction

Automated visual testing presents a solution to the challenges of manual visual testing. By leveraging testing tools, automated visual testing can capture, compare, and detect even the tiniest visual discrepancies between two images, surpassing the ability of human observation. Additionally, automated visual testing can log and report defects and repeat tests as often as needed without additional manual effort. Overall, it can save time, increase efficiency, and improve the accuracy of the testing process.

The Pros and Cons of Visual Testing That Testers Must Be Aware Of

Pros

Visual testing techniques offer several advantages, including faster bug recognition and regression prevention. The process encourages developers to maintain a higher UI quality across various devices while providing valuable feedback to designers for optimizing designs on multiple screen sizes and resolutions. Visual testing can also identify edge cases related to specific views on specific devices that may be difficult to discover manually. 

Key benefits of visual testing include:

• Quick detection of bugs
• Improved code and UI quality
• Reduction of regressions
• Control of views on different devices
• Valuable feedback for designers
• Code reduction
• Low entry barrier, making them easy to implement
• The lower workload for testers
• Fast implementation
• Automation

Cons

• Testing software applications on various screens with different resolutions is practically impossible, which can leave some bugs untested despite rigorous visual testing.
• Minor UI issues that are difficult for the human eye to detect can remain unnoticed during visual testing, with only more significant bugs being identified.
• Inconsistencies may arise in identifying bugs among different testers.
• Bugs may also depend on various factors such as time, network, and device, leading to more time being spent on fixing bugs that are working fine in a real environment.
• Automated visual testing requires significant maintenance, making it impractical for small projects.

Learn more: Introducing the HeadSpin Audio-Visual Platform

Must-have Tools for Developers to Conduct Seamless Visual Testing

Visual testing is crucial for ensuring the quality and consistency of user interfaces in mobile app development. Here are some popular visual testing tools that developers can leverage for Android, iOS, and React Native platforms:

Android

• Screenshot-tests-for-android: Developed by Facebook, this tool allows developers to capture screenshots of entire screens or individual UI components and compare them with reference images to detect any visual differences. It also provides support for continuous integration and generates test reports.

iOS

• iOSSnapshotTestCase: Initially created by Facebook as FBSnapshotTestCase and now supported by Uber as iOSSnapshotTestCase, this library provides a reliable solution for visual testing on iOS devices. It allows developers to capture screenshots and compare them with reference images to detect any visual changes.

React Native

• Jest: Widely used for snapshot testing in React Native, Jest allows developers to detect unexpected changes in the interface, including UI, API responses, error messages, and logs. It offers a straightforward and efficient approach for conducting visual testing in React Native applications.

• Loki: Although mainly used in web development, Loki also has applications in React Native. It allows developers to capture screenshots and compare them with reference images, but it may require additional customization for effective testing, such as using the same simulator with the same screen size.

Learn more: Visual Testing with Appium - Part 1

Why Testers Should Implement Visual Testing in Agile Environments

Visual testing is a critical component of agile methodologies that ensures a consistent user experience across devices and browsers. This method should be integrated into story/epic sizing in sprints/releases, with visual element attributes identified for coverage, including layout, colour, and typography. Additionally, visual testing should be included in feature tests of the automation test suite, with unique identifiers for issue traceability and analysis. Visual tests with functional validations should be flagged with business priority to ensure functional tests pass while identifying visual issues. Furthermore, visual testing can form the basis for accessibility tests, as visual cues are integral to accessibility validation.

The focus of visual tests should encompass colors, content/layout (hidden components, incorrect overlays, resizing effects, zero-pixel iframe, and iframe interactions), and dynamic data (smart lists and signal strengths for mobile).

However, automated visual testing poses challenges, such as the time-intensive process of creating tests for complex UI elements, cross-browser compatibility issues leading to false positives or negatives, and heightened test maintenance requirements, especially when UI or code updates necessitate updates to baseline images.

How to Choose a Visual Regression Testing Tool?

Choosing the right visual regression testing tool is crucial for ensuring user interface (UI) quality and consistency across different devices and platforms. Here are some detailed considerations to keep in mind when selecting a visual regression testing tool:

• Integration Capability: Your chosen tool should integrate effortlessly with your current tech stack. This includes compatibility with your project management tools, version control systems, and CI/CD pipelines. A good fit reduces setup time and improves workflow efficiency.
• Ease of Use: Usability is key, especially if the tool will be used across multiple teams or by team members with varying levels of technical expertise. Tools with a user-friendly interface, clear documentation, and strong customer support make the process smoother and more efficient.
• Scalability: The tool you select should be able to handle your current requirements and grow with your project. It should support increasing test volumes without performance degradation and allow you to add more projects or expand test coverage.
• Cross-Platform Support: The tool must support all the platforms (like Windows, macOS, Linux) and devices (desktop, mobile, tablet) on which you wish to run your applications. This ensures your visual regression tests cover all user scenarios and device configurations.
• Reporting Features: Effective reporting features are crucial for a regression testing tool. The tool should provide clear, actionable reports that help developers quickly identify and address issues. Look for tools that effectively highlight visual differences and integrate these insights into your review process.

Manual vs. Automated Visual Regression Testing

Manual Visual Regression Testing involves human testers looking at the application to determine if the visual aspects are as expected. This method, though straightforward, hinges on the tester's ability to catch subtle differences and inconsistencies by eye.

Advantages of Manual Testing:

• Human Insight: Human testers can understand and interpret visual elements in ways that might be difficult for automated tools, particularly when understanding the context or the aesthetic feel of the user interface.
• Flexibility: Manual testing can be more flexible in adapting to last-minute changes in design without the need to reconfigure tools or scripts.

Disadvantages of Manual Testing:

• Time-Consuming: Manual testing requires significant time, especially with complex applications that must be tested across various devices and operating systems.
• Subjectivity: Different testers might have varying standards for what constitutes an issue, leading to inconsistencies in test outcomes.
• Scalability Issues: Manual testing becomes less feasible as the project grows due to the increasing effort and time required.

Automated Visual Regression Testing, on the other hand, uses tools to automatically capture and compare screenshots of the app across different devices and OS. This method leverages technology to spot differences, aiming to reduce human error and increase efficiency.

Advantages of Automated Testing:

• Efficiency and Speed: Automation can execute many tests in a fraction of the time it takes manual testers.
• Consistency: Automated tests ensure consistent and repeatable testing by executing the same steps in the same manner every time.
• Integration with DevOps: Automated tests can be integrated into continuous integration and deployment pipelines, allowing for ongoing quality assurance during development cycles.

Disadvantages of Automated Testing:

• Initial Setup and Cost: The initial setup for automated testing can be complex and costly, often requiring purchasing tools, setting up environments, and scripting tests.
• Maintenance: Automated tests can be brittle and may require frequent updates whenever there are changes to the application's user interface, which can be resource-intensive.

Choosing the Right Approach:

The decision between manual and automated visual regression testing often depends on several factors, including the project's scale, budget, timeline, and the criticality of the application's visual appearance. Manual testing might suffice for small-scale projects, but automated testing becomes indispensable for larger, more complex applications, particularly those where visual consistency is critical across various platforms.

By combining both approaches—leveraging automated testing for broad, repetitive tasks and manual testing for nuanced, complex scenarios—teams can ensure comprehensive coverage and maintain high-quality standards in the application's visual presentation.

Visual Regression Testing and AI: What's Next?

The integration of AI into visual regression testing is poised to revolutionize how developers handle UI consistency across versions and devices. AI can automate complex visual tasks that were previously challenging to automate. For example, AI algorithms can now more accurately detect and analyze visual differences imperceptible to the human eye.

Future Trends:

• Advanced Anomaly Detection: AI can learn from historical data to more efficiently predict and detect anomalies in visual aspects.
• Self-Healing Tests: AI can automatically adjust test scripts based on detected UI changes, reducing the maintenance burden.
• Enhanced Test Coverage: AI can suggest new test cases based on understanding the application's visual elements and user interactions.

Integrating AI into visual regression testing tools enhances their effectiveness and supports more agile, responsive development practices. This AI-driven approach is set to become a standard in ensuring top-notch UI/UX consistency in app development.

Gain a Competitive Edge with HeadSpin's Advanced Audio/Visual Testing Capabilities

The HeadSpin AV Platform is a cutting-edge technology that enables "black box" visual testing by capturing the user's experience on real media devices, providing true cross-browser and device compatibility. It is suitable for various use cases, such as OTT/media, live streaming, accessibility, voice activation, video calling, communication, camera, and voice assistants, along with critical media content such as DRM-protected content. By performing automated visual testing on the HeadSpin AV Platform, businesses can gain insights into critical KPIs, including video MOS, frame rate, blurriness, downsampling index, and Netflix VMAF. Our framework-agnostic platform offers OCR analysis tools for features such as closed captioning, fast-forwarding, rewinding, and returning to video at the correct time. Additionally, the HeadSpin AV Platform offers location testing for diverse device locations to compare results and detect localization issues.

HeadSpin's AV Platform provides you with the ability to:

• Ensure your apps function as intended on targeted devices, including media devices.
• Verify that speakers and microphones function correctly on the devices themselves.
• Get high-quality audio and video performance.
• Achieve compatibility across multiple applications, operating systems, devices, and carrier networks.

Case Study

HeadSpin helped an American multinational mass media company evaluate streaming KPIs and analyze performance across global locations. By deploying 100+ devices, including mobile, OTT media devices, and smart TVs, they enabled functional and content evaluation for various applications. They also provided test automation, granular data, and comprehensive dashboards for effective evaluation. HeadSpin partnered with the client to build solutions for content evaluation on smart TVs and evaluate all prevalent streaming devices. As a result, the client achieved quick turnarounds for the evaluation process through automation and analytics.

Learn more: Visual Testing With Appium - Part 2

Summing It Up

Visual testing is a critical aspect of software testing responsible for creating a positive user experience. As the UI of an application creates the first impression on customers, visual testing plays a crucial role in ensuring that the application is user-friendly and meets the expectations of its users. With the increasing importance of online presence, big IT companies are hiring professional testers to perform visual testing, which is becoming more automated, faster, and accurate. Visual inconsistencies can significantly impact the user journey, so it is essential to eliminate them quickly and comprehensively through visual testing. As a result, visual testing is now an indispensable method for any enterprise that wants to establish an online presence.

When conducting visual tests, it is essential to use a real device cloud to obtain precise results. HeadSpin offers a global device infrastructure, allowing testers to perform automated visual UI tests in real user conditions across 90+ global locations. 

Originally published at - https://www.headspin.io/blog/effective-visual-testing-methods-for-successful-app-development