In these intensely competitive times, every application needs to be aesthetically appealing in addition to providing seamless functionality to satisfy the users. If an app does not function properly because of a visual interface error or if the site is unreadable, users may uninstall or leave the app. This can ultimately lead to a loss of customers and a drop in revenues. To avoid this and ensure the best possible user experience, companies are prioritizing visual regression testing.
Let us study a couple of examples of visual regression testing to understand its importance:
These UI errors need to be fixed since changes in the elements can lead to the breakdown of the visual aesthetics. These illustrations clearly highlight the importance of conducting thorough visual regression testing before software releases. Visual regression testing checks the UI of an application after code changes and ensures that these different visual elements, like buttons, images, and text, all render correctly.
Through this blog, we will examine visual regression, the different techniques used, the challenges testers face when performing the tests, and the best practices to overcome them.
Visual testing ensures that visual glitches are identified early and do not spill over to the production stage. Let us now briefly examine the commonly used methods for regression testing:
Manual regression testing is recommended in the early testing stages when the UI is unstable. It is suitable for ad hoc testing and conducting quick on-the-spot checks. In this method, the testers manually check the layout and appearance of the visual elements. It involves taking the latest screenshots and comparing them with baseline screenshots to unearth any gaps.
The manual technique can be time-consuming and lead to a waste of resources because of its repetitive nature. The scale of manual testing grows with every new feature addition, thereby increasing the time spent by a human tester pouring over screenshots to identify bugs. It is impractical to manually conduct visual regression testing when there are tight deadlines to meet.
In contrast, automated visual regression testing helps you automate repetitive tasks like checking the page following every code change. Even with the heavy initial investment, automated testing, especially with AI-powered features, helps you save time and money in the long run.
Though automated visual regression testing reaps many benefits for testers and developers, it has to be properly implemented if they are to receive its benefits. Let us examine the best practices that go into a proper visual regression testing process.
Following these best practices will help optimize your automated visual regression testing process and help you gain its maximum benefits:
Clearly define what to record in the snapshots and the time to take them while testing. Remember to include user interactions in such scenarios since the applications will need to deal with them in the real world.
Instead of only validating individual components, ensure to validate the entire UI page. This practice will increase the test coverage and improve it.
The visual regression testing tool you choose should be capable of ignoring false positives. It should be able to take care of pixel offsets, anti-aliasing, and other issues while ensuring the tests don’t fail. The automated algorithm must be able to analyze the page’s structure and perform layout comparisons. It should also be able to take care of content that is dynamic and moving.
Testers should compare screenshots with recent ones to identify the disparities. This will help ensure that the UI remains the same, improving the user experience and product quality.
Ensure to resolve bugs that the testing tool identifies or send them to the respective developers for fixing them. Remember to update screenshots to be used as a baseline for future regression tests after it is done.
Automated visual regression testing may not be appropriate for all scenarios. In some situations, using a combination of manual and automated methods is advisable. For example, when new features are added, they can be validated using an automation technique and then checked manually.
A team of reviewers or a single reviewer can examine how adjustments are made without causing interruptions in the desired outcome and make a report accordingly.
Learning from your successes and failures is crucial in visual regression testing. You need to identify the root causes and impacts of the visual changes detected by your tool. Testers must also identify false negatives and positives and adjust the tool settings and test cases accordingly.
In conclusion, successful implementation of visual regression testing in Agile teams requires a holistic approach that encompasses automation, collaboration, scalability, and comprehensive analysis. Visual regression testing can be a game-changer if you choose an appropriate automated testing tool with the right features and advantages.
By adhering to these best practices, Agile teams can effectively leverage visual regression testing to deliver high-quality user interfaces in an iterative and efficient manner.