AI has significantly transformed various industries worldwide, presenting positive and negative impacts. While there’s room for improvement in this revolutionary technology, businesses that fail to adopt it risk falling behind. AI enhances visual testing by improving accuracy, speed, adaptability, and the overall efficiency of detecting visual defects in software applications or websites.
This article delves into AI testing, examines how AI can enhance testing practices and presents cutting-edge AI testing tools that can revolutionize QA workflows.
What constitutes visual regression testing?
Visual regression testing, sometimes called user interface (UI) testing, involves validating the visual accuracy of all elements visible and interacted with by end-users after implementing code modifications on a website. This differs from functional testing, which focuses on ensuring the proper operation of the application’s functions and features.
Visual regression tests aim to identify visual discrepancies, often termed ‘bugs,’ that functional testing tools might overlook. These discrepancies encompass issues like misaligned buttons, overlapping text or images, partially displayed elements, challenges with responsive layouts and rendering, and similar visual anomalies.
Integrating AI in Visual Testing
This has sparked a transformation in the testing landscape. Traditional testing methods relied on manual human inspection or test scripts crafted through automation frameworks like Selenium. However, relying on selectors, these scripts often encountered issues when the application code evolved, resulting in false negatives. This necessitated frequent script revisions or the creation of entirely new scripts, leading to iterative debugging and time-consuming delays.
AI presents a more effective solution. Several testing tools leverage AI in Visual testing, employing advanced “Visual Locators” that offer enhanced robustness, eliminating the pitfalls associated with rigid selector-based approaches. By utilizing AI in Visual Testing to visually locate elements akin to human perception, changes in element selectors do not pose significant challenges as the AI can visually identify them.
Use of Visual AI
Visual AI is presently extensively utilized and can significantly influence numerous markets and industries. If you’ve ever unlocked your phone using Apple’s Face ID, experienced automatic image labeling in Google Photos, or purchased at a cashier-free store like Amazon Go, you’ve interacted with Visual AI.
Advancements in Visual AI power various technologies, including self-driving cars, medical image analysis, sophisticated image editing tools, and software visual testing for bug prevention.
Limitations of Snapshot Testing
Snapshot testing, frequently employed to assess the appearance of an application, aims to identify any visual alterations and ensure the absence of visual regression.
However, this method has its limitations.
Snapshot testing relies on baseline snapshots for comparisons, often scrutinized at the pixel level, resulting in numerous false positives due to the following reasons:
- Anti-Aliasing Effects: Anti-aliasing is used to smooth the jagged edges of images. Varied anti-aliasing settings across machines conducting snapshot tests could inaccurately tag snapshots as changed, leading to false positives.
- Dynamic Content: Certain sections of an application, like changing counts or personalized recommendations, are designed to alter over time. However, in snapshot testing, such changes might be wrongly flagged as alterations, causing false positives.
- Browser Variability: Diverse browsers might render images and fonts differently due to variations in their rendering engines, leading to inconsistencies and false positives in comparisons.
These factors contribute to the dissatisfaction among QA test engineers with snapshot testing, as it generates an overwhelming number of false positives requiring manual sorting.
While AI in Visual Testing has significantly progressed the landscape of visual regression testing, there is an urgent need for tools leveraging AI in visual testing capable of conducting sophisticated cosmetic testing with enhanced change detection capabilities.
How Visual AI Operates?
Visual AI resolves the drawbacks of pixel and DOM techniques by identifying the visual elements constituting a screen or webpage. Instead of analyzing individual pixels, Visual AI employs computer vision to recognize elements as objects with various attributes (size, color, content, position, etc.), akin to human vision. It then uses these attributes to compare checkpoint elements against baselines, detecting visible differences.
Workflow of the Visual AI Solution
Data Collection and Preprocessing
After each successful release, the solution collects baseline UI and DOM for future tests. Screenshots and DOM of modified web pages are taken when new changes are applied. Before model input, images undergo preprocessing, correcting size, orientation, and color for comparable baseline and current images.
Element Classification
Initially, the model examines DOM elements relevant for visual testing, learning from previous tests. It identifies and filters out irrelevant elements, scrutinizing baseline and test DOMs for changes, additions, and deletions of these visual elements.
Visual AI Locators
Next, the Visual AI model uses computer vision to locate predefined visual components (e.g., buttons, tables) on web pages. It learns these locators, scanning modified screenshots for their presence. If a locator is missing from the modified screenshot compared to the baseline, the model flags it as a defect.
Visual Differences
Subsequently, the model compares locators and elements from the baseline and modified pages to identify visual disparities. Figure 6 showcases baseline and current screenshots with highlighted differences, such as overlapping text and missing buttons, detected by the Visual AI solution.
The Visual AI solution generates a comprehensive report containing page screenshots, clearly marking visual differences.
It can be run in batch mode, supporting tests across multiple web pages, browsers, screens, and operating systems.
Detected defects are directed to the development team for resolution. Additionally, feedback on test accuracy from the testing team is used for continual model retraining and enhancement.
The Visual AI approach surpasses pixel and DOM-based tests as it:
- Ignores minor visual discrepancies caused by slight pixel shifts across different browsers during rendering
- Differentiates between visual and non-visual elements, resembling human discernment
- Recognizes permissible movement of visual elements on a page, disregarding them across screens
How Visual AI Transforms Software Development and Testing Today
There’s a heavy reliance on manual testing in conventional software testing methodologies. Even within organizations equipped with sophisticated automated testing frameworks, validating the entire digital experience, encompassing functional, visual, and cross-browser testing, has been a persistent challenge in the realm of automation.
Without an efficient means to validate the entire web page, automation engineers are entangled in the laborious task of scripting intricate locators and assertions for every element under evaluation.
Even after this effort, quality engineers and other software testers spend considerable time meticulously reviewing their screens to ensure that no new bugs have surfaced with the latest release. This meticulous process is repeated across various platforms, browsers, and, at times, every individual device used by their clientele.
Meanwhile, software development is becoming increasingly intricate. Applications now feature more pages, and the pace of releases has accelerated, often adopting continuous deployment practices. Consequently, tens or even hundreds of thousands of potential screens could demand testing.
Linear scalability, the hallmark of traditional testing methods that hinge on allocated resources, struggles to cope with this burgeoning demand. Organizations relying on traditional methods face the dilemma of either slowing down releases or curtailing their test coverage.
How does Visual AI aid in cross-browser testing?
Owing to its exceptional precision and effective validation of the complete screen, Visual AI introduces opportunities to streamline and expedite the complexities associated with cross-browser and cross-device testing.
By adopting a ‘rendering’ rather than ‘executing’ approach across diverse device and browser combinations, teams can swiftly obtain test outcomes through the LambdaTest Cloud, surpassing the speed of conventional execution grids or device farms.
LambdaTest is an AI-powered test orchestration and test execution platform that enables users to test across 3000 diverse operating systems and real devices. It offers both manual and automated testing.
What lies ahead for Visual Regression Testing?
Our world is swiftly evolving into one heavily reliant on applications, where individuals increasingly depend on technology for various aspects of their lives, spanning entertainment, work, leisure, and hobbies. The proliferation of applications has surged, necessitating a seamless omnichannel user experience as an essential requirement rather than a mere luxury.
Furthermore, experiences now hold more significance in user engagement than just functionalities. Adhering to the conventional human-centered approach to visual testing is impractical, making AI-powered test automation imperative for the future. Introducing AI at the heart of visual testing resolves numerous challenges organizations currently encounter.
Conclusion
Today, a significant portion of the visual information we encounter exists in digital formats. Whether accessed through a desktop, laptop, or smartphone, individuals and businesses depend on extensive computing capabilities and access to millions of user-friendly applications.
The contemporary digital landscape, saturated with vast amounts of visual data, owes much of its existence to the assistance provided by artificial intelligence. Visual AI, or the capacity of computer vision to interpret images akin to humans, plays a crucial role. As digital content continues to prioritize visuals, the significance of AI in comprehending and managing images on an extensive scale has grown immensely.
AI-driven test automation isn’t merely a theoretical concept but rather a practical solution aligned with core business requirements. It has the potential to scale effectively, laying the groundwork for the next generation of test automation.