The Power of Visual Regression Testing_ Why It Is Critical for Enterprise Apps.pdf

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.

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.
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.
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.
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.
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.
Article resource:
This article was originally published on:
https://www.headspin.io/blog/effective-visual-testing-methods-for-successful-a
pp-development

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