Bertha WhiteDid you know 73% of consumers believe a good experience is key in influencing their brand loyalties? Today, the way the end users perceive your software product is no longer determined solely by its functionality, performance, and security but by the overall experience.
Visual testing or visual regression testing is a gateway to address this, scrutinizing the user interface of a software application and helping enhance user experiences. This meticulous process is designed to uncover visual discrepancies and defects arising from issues like incorrect styles, misalignments, or font irregularities. It achieves this by conducting a pixel-by-pixel comparison of two snapshots, ultimately yielding a detailed report highlighting the differences.
While visual testing is a potent quality assurance tool, it can occasionally yield false positive results, posing challenges for software teams. In this article, we will walk through some effective strategies to minimize these discrepancies and ensure that your visual validation testing process remains reliable and insightful.
How do visual errors impact user experiences?
Visual errors, when allowed to persist without remediation, wield a substantial and adverse impact on the user experience. These errors materialize in various forms, from misaligned elements that disrupt the harmonious layout to fonts exhibiting inconsistency that disrupts readability. Moreover, deviations from prescribed styles can introduce disarray, causing user frustration and eroding overall satisfaction levels.
As user interfaces play a pivotal role in shaping digital interactions, these visual discrepancies assume heightened significance. They can hinder the user's ability to navigate smoothly, comprehend content effortlessly, and engage with the software product intuitively. As such, addressing and rectifying these visual imperfections become not only a matter of good practice but a strategic imperative for businesses seeking to cultivate user loyalty and promote positive brand associations.
What do Visual Bugs Look Like?
Visual bugs encompass a broad spectrum of issues, such as distorted images, layout misalignments, broken links, and inconsistent color schemes.
Identifying and rectifying these issues is crucial for maintaining a polished and user-friendly interface.
Understanding false positives in visual testing
The concept of a false positive emerges as a critical phenomenon. It characterizes a scenario where the visual testing process, albeit well-intentioned, mistakenly identifies an element as a defect or discrepancy despite the absence of any actual issue. This phenomenon, while not uncommon, holds substantial implications for the efficacy and resource management within the quality assurance framework.
Distinguishing between two fundamental categories—true positives, where legitimate defects are correctly identified, and false positives—is a foundational endeavor in visual testing. This distinction transcends mere technicality; it represents the bedrock upon which testing efficiency and precision stand. The ability to separate genuine issues from false alarms serves as a compass, guiding testing efforts toward the areas that genuinely require attention. This discernment is pivotal in the realm of quality assurance, as it preserves resources, avoids unnecessary remediation, and ultimately fortifies the integrity of the testing process. Consequently, it elevates testing efficiency, ensuring that the rigorous pursuit of visual perfection remains both effective and judicious.
Why do false positives emerge in visual testing?
The phenomenon of false positives in visual testing is a multifaceted challenge, often rooted in several contributing factors. An in-depth comprehension of these factors is instrumental in mitigating false positives and fostering a more refined testing environment. Let's delve into the key elements that play a pivotal role:
1. Rendering discrepancies across browsers and devices:
The diversity of browsers and devices in use today introduces subtle variations in rendering web content.
Differences in how browsers interpret CSS, HTML, and other web technologies can lead to discrepancies that trigger false positives.
2. Dynamic content generation:
Modern web applications often rely on dynamic content generation driven by user interactions or real-time data updates.
These dynamic changes can disrupt the pixel-perfect comparisons performed in visual testing, occasionally flagging elements as defects when they are, in fact, responsive to user actions.
3. Minor visual variations:
Visual testing is highly sensitive to even the slightest deviations in pixel values or element positioning.
Minor variations caused by factors such as anti-aliasing, sub-pixel rendering, or font rendering can occasionally result in false positive outcomes.
4. Rapid development and continuous integration:
Agile development methodologies and continuous integration practices emphasize frequent code updates and releases.
This rapid pace can introduce changes that affect the visual layout, increasing the likelihood of false positives as new code interacts with existing design elements.
5. Lack of baseline image maintenance:
Without regular updates to baseline images, visual testing may compare against outdated references, triggering false positives when legitimate design changes have occurred.
6. Tolerance thresholds and configuration:
The sensitivity settings and tolerance thresholds configured in visual testing tools play a pivotal role.
Inadequate calibration can lead to overly stringent comparisons and an increased propensity for false positives.
Minimizing False Positives in Visual Validation Testing
Reducing false positives in Visual Validation Testing (VVT) necessitates a comprehensive approach, particularly when dealing with dynamic content and baseline images. Here are effective strategies with illustrative examples:
1. Dynamic content handling:
a. Wait for element stability:
Dynamic content often appears or changes in response to user interactions.
Employ explicit waits to ensure element stability before capturing a screenshot.
Example using Selenium in Python:
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
Wait for dynamic content to be stable
wait = WebDriverWait(driver, 10)
element = wait.until(EC.presence_of_element_located((By.ID, 'dynamicElement')))
driver.save_screenshot('screenshot.png')
b. Validate dynamic elements:
Check dynamic elements' properties, such as text or attributes, to ensure they match expected values.
Example using JavaScript and WebDriverIO:
const dynamicElement = $('#dynamicElement');
const expectedText = 'Expected Text';
if (dynamicElement.getText() === expectedText) {
browser.saveScreenshot('screenshot.png');
}
2. Baseline image management:
a. Regularly update baseline images:
Baseline images should reflect the current expected state of your application.
Example using Selenium WebDriver in Python:
from selenium import webdriver
Initialize the WebDriver
driver = webdriver.Chrome()
Navigate to the application page
driver.get('https://example.com')
Take a screenshot to create/update the baseline image
driver.save_screenshot('baseline.png')
Close the WebDriver
driver.quit()
b. Implement version control:
Employ version control systems like Git to manage baseline images, enabling easy tracking of changes over time.
Example Git command to commit baseline image updates:
git add baseline.png
git commit -m "Update baseline image"
c. Tolerance thresholds:
Set tolerance levels when comparing current screenshots with baseline images to account for minor visual variations.
Example using Applitools Eyes (JavaScript):
eyes.setMatchLevel(MatchLevel.Layout); // Set matching level with layout
How does HeadSpin help streamline visual validation testing for improved user experience?
HeadSpin’s data science driven Platform enables businesses to test websites and apps and monitor critical KPIs that impact user experience. The Platform supports parallel testing on multiple devices and browsers simultaneously. This accelerates the testing process, enabling you to execute visual tests quickly and efficiently.
How does it benefit?
● User-centric performance testing
By simulating real user interactions and network conditions, HeadSpin helps you evaluate the impact of performance on the user experience. It identifies visual anomalies related to slow loading times or other performance bottlenecks.
● Regression monitoring
HeadSpin's regression intelligence capabilities detect and highlight even minor visual regressions, ensuring that any changes to your application's appearance are promptly identified. This proactive approach helps maintain a polished user interface.
● Custom KPIs and analytics
The platform provides detailed visual analytics and reporting, giving you a deeper understanding of how users perceive your application's visual elements. A data-driven approach like this helps you make informed decisions to enhance the user experience.
● Seamless integration with UX tools
HeadSpin seamlessly integrates with user experience (UX) and quality assurance tools, facilitating collaboration between development, testing, and design teams. This integration ensures that user-centric visual validation is an integral part of your development process.
● Automated alerts
HeadSpin offers automated alerts for visual anomalies, allowing you to address issues as soon as they arise. This quick response helps prevent negative user experiences resulting from visual defects.
In a nutshell
Visual testing represents a pivotal dimension of quality assessment, enhancing value management and reinforcing the shift-left approach in software delivery. It stands as a beacon of efficiency, accelerating delivery timelines, bolstering resource allocation, and instilling confidence in software quality through a 'create once, run everywhere, often' ethos.
Moreover, the creation of baseline images as artifacts from release cycles extends beyond quality assurance. These images serve as invaluable references for scrutinizing user experiences, fueling in-depth analytics encompassing usability, accessibility, and broader business initiatives.
Original source: https://www.headspin.io/blog/reducing-false-positives-in-visual-testing
