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Friday, July 12, 2024

Common Mistakes to Avoid in Visual Regression Testing

Visual regression testing is crucial to modern web development and software engineering. It ensures that changes to a website or application do not unintentionally alter its appearance or functionality. Despite its importance, many teams struggle with visual testing due to common mistakes that can lead to inaccurate results and wasted time. 

In this blog, let us explore some common mistakes and provide tips on avoiding them to improve your visual regression testing process.

Visual Regression Testing

Visual regression testing is a quality assurance concept used in software development to uncover unintentional visual deflections in web applications or user interfaces. It is about taking screenshots of web pages or UI components before and after code changes and then comparing them pixel by pixel to find the differences. It can be automated to prevent a change in the visual aspect or the layout of an app when released from a new build.

Visual testing helps maintain consistency in design across different browsers, devices, and screen sizes by improving the user experience and preventing visual defects from reaching production environments.

Common Mistakes to Avoid in  Visual Regression Testing

Let us see some common mistakes in visual testing and its solutions.

Neglecting Proper Baseline Setup:

One of the most typical problems is that not having an effective baseline for visual regression testing can create a bad situation that can affect the accuracy or performance of the whole testing mechanism. Lacking a good reference point against which one can compare, visual inconsistencies and code effects result in difficulties in accurately identifying discrepancies and assessing the impact of code changes.

Spending time on the establishment of a reliable baseline is crucial. These screens capture the important pages or every essential element in the real state before implementing any major changes to the app or website. These snapshots from the baseline serve a purpose in future comparisons, serving as a pivot point of reference for evaluating visual modifications over time.

Baseline stabilities will help visual testing to give accurate verdicts on what the design and utilization of the program or website should be. This consistency helps increase the reliability of test results. It enables the teams to effectively track the causes of any visual mistakes by enabling them to pinpoint the source of such issues more quickly. 

Visual testing attains its increased effectiveness with the well-defined baseline that allows teams to sustain the visual quality of their products and provide the user with a high-quality experience.

Lack of Comprehensive Test Coverage

Failing to ensure comprehensive test coverage in visual testing can lead to overlooking critical visual discrepancies and potential issues across different components and pages of the application or website. This oversight may result in unexpected visual bugs slipping into production, compromising user experience and damaging the product’s reputation.

Identifying functionalities to address these challenges, layouts, and pages most susceptible to visual changes are crucial. Prioritizing these areas enables teams to focus their testing efforts effectively and ensure that the most critical aspects of the application receive thorough attention.

Developing a comprehensive test suite is essential to achieve thorough coverage. This suite should encompass various screen sizes, browsers, and devices commonly used by the target audience. Testing across diverse environments helps uncover potential visual discrepancies that may arise due to differences in rendering engines or device characteristics.

Prioritizing critical components and pages and developing a comprehensive test suite helps mitigate the risk of visual defects and ensures a consistent and high-quality user experience across different cloud platforms and devices. 

One such clod platform that can be used is LambdaTest. It is an AI-powered test orchestration and execution platform to run manual and automated tests at scale. The platform allows you to perform both real-time and automation testing across 3000+ environments and real mobile devices. LambdaTest extends its testing capabilities to real mobile devices, providing a realistic environment for testing mobile applications. This includes testing various devices, operating systems, and screen sizes, addressing the complexities of mobile app development.

This approach to test coverage enhances the effectiveness of visual testing and strengthens the overall quality assurance process.

Relying Solely on Automated Tools

Automated visual testing tools offer efficiency and scalability, yet relying solely on them risks overlooking implied visual changes that automated algorithms might not detect. While these tools excel in detecting obvious disparities, they may struggle with discerning variations that could still impact user experience.

A pragmatic solution involves integrating automated tools with manual inspection. Human evaluators can provide invaluable insights by utilizing their intuition and domain expertise to detect inconspicuous visual anomalies. Their ability to contextualize changes within the broader design framework enhances the thoroughness and accuracy of the testing process.

Incorporating human oversight ensures a more comprehensive assessment of visual integrity, reducing the likelihood of critical issues slipping through undetected. Additionally, human involvement fosters collaboration and knowledge sharing within the team, enriching the testing process with diverse perspectives and insights.

Encouraging a symbiotic relationship between automated tools and human evaluation elevates the effectiveness and reliability of visual testing, fortifying the overall quality assurance strategy.

Ignoring Dynamic Content and States

Dynamic content such as user-generated data or animations can pose challenges for visual testing if not appropriately handled. To address this issue, teams must effectively implement strategies to manage dynamic content and states in their tests.

One strategy involves generating consistent test data to ensure uniformity across test runs. By using predefined datasets, teams can minimize discrepancies caused by variations in user input. Additionally, incorporating wait states for animations to complete before capturing screenshots can help ensure accurate comparisons between different application states.

The test process of visual regression for dynamic content should be carried out with much thought about how changes in data or animations could impact the look of the application. Prioritizing the implementation of these strategies, teams can improve the reliability and efficiency of their visual regression testing techniques, resulting in reduced chances of wrong positives or negatives in their test results. This way of approaching problems earlier ensures that issues related to visual inconsistency are better recognized and managed earlier, which leads to the increased quality and consistency of a web application or software product.

Overlooking Environmental Discrepancies

Discrepancies in testing environments, like variations in browser versions or screen resolutions, pose significant risks to the accuracy of visual regression tests. Such inconsistencies can lead to false positives or negatives, where visual changes are incorrectly flagged or missed.

To mitigate these challenges, teams must prioritize standardizing testing environments. This involves employing tools such as virtual machines, containers, or cloud-based services to create uniform testing environments across the development and testing lifecycle. By doing so, teams can ensure consistency in browser versions, screen resolutions, and other environmental factors.

Standardization reduces the chance of acquiring false results and facilitates the testing process. It enables developers and testers to detect and fix the gaps or issues observed at the visual level, assuredly, as the development environment simulates real-world conditions. It creates collaboration as it provides a common set framework for testing and promoting the free flow of information between the team members.

By employing standardized testing environments, teams can improve the confidence that comes from visual testing. Adopting this strategic methodology prevents the impact of environmental deviations, which guarantees the delivery of web apps and software products at a high-quality level and with a high level of confidence and stability.

Failing to Maintain Test Suites

Visual regression test suites ensure the integrity of web applications or software products. However, failing to maintain these suites can render them ineffective over time as applications evolve.

To address this issue, teams must establish a culture of regularly reviewing and updating test suites. This involves assessing the relevance and coverage of existing tests in light of changes to the application’s design or functionality. By identifying outdated or redundant tests, teams can streamline their testing efforts and focus on the most critical areas.

Not Investigating Failures Thoroughly

If the test of visual regression fails, it is vital to provide a fault-finding of the root cause to avoid recurrent issues. Creating a worthwhile and working debugging process for failed tests is one of the musts for providing dependability and correctness of the tests.

Firstly, we need to put down all data that can be used to identify the failure, e.g., screenshots, error logs, and relevant metadata. Thus, this data becomes the base for identifying the class and scope of the problem, leading to debugging.

Next, reviewing recent changes in the application’s codebase or design is essential to identify potential triggers for the failure. By correlating test failures with recent modifications, teams can identify the specific changes that may have introduced the issue.

Collaboration between developers and designers is also vital in investigating test failures thoroughly. By utilizing their expertise and perspectives, teams can analyze the failure from multiple angles and devise effective solutions collaboratively.

By establishing a proactive approach to debugging and issue resolution, teams can minimize downtime and ensure the continued reliability of their visual regression testing efforts.

Ignoring Cross-Browser and Cross-Device Compatibility

Browser and device testing of visual regression is skipped, it may result in user experience being incongruent. Users might encounter layout deformations or incompatible functionalities when accessing the application or website from different platforms.

To address this challenge, teams should prioritize testing across various browsers (Chrome, Firefox, Safari, and Edge) and devices (desktops, tablets, and mobile phones). This ensures that the visual integrity of the application remains consistent regardless of the platform used.

To ensure proper rendering of visual elements on multiple browsers and devices, teams can integrate cross-browser and cross-device compatibility testing into their visual testing strategy. This way, they can catch and fix such issues as browser rendering differences or device-specific behavior bugs and deliver a smooth user experience to all platforms.

Conclusion

In conclusion, visual regression testing is critical to modern web development and software engineering, ensuring the integrity of applications’ visual appearance and functionality. By avoiding common mistakes such as neglecting baseline setup, lacking comprehensive test coverage, relying solely on automated tools, and ignoring environmental and cross-browser compatibility, teams can enhance the effectiveness and reliability of their testing efforts. Embracing best practices and proactive strategies for maintaining test suites, investigating failures thoroughly, and prioritizing cross-browser and cross-device compatibility testing is essential for delivering high-quality web applications and software products that meet user expectations.

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