Why Relying on Manual Tolerance or Thresholding in Visual Testing Tools is a Bad Idea
Tags: visualtesting, ai
Published: 2024-06-14 01:28:30

Ensuring that applications look and function as expected is crucial in today’s competitive market. Visual testing tools have become essential in this process, helping teams catch visual discrepancies that automated functional tests might miss. However, not all visual testing tools are created equal, and one significant drawback is the reliance on manual tolerance or thresholding settings. This approach can lead to numerous issues, ultimately increasing the effort required to maintain the application.

##The False Positives Nightmare
Setting the tolerance or threshold too strictly in a visual testing tool often results in a high number of false positives. A false positive occurs when the tool identifies a change that isn't a defect. For example, a slight shift in the position of an element or a minor color difference due to browser rendering could be flagged as an issue.

These unnecessary alerts can quickly become overwhelming for the development team. Every false positive requires manual inspection to determine whether it's a real issue or just an innocuous change. This process is time-consuming and frustrating, pulling developers away from more critical tasks and reducing overall productivity. Instead of streamlining the testing process, strict thresholding can make it more cumbersome and error-prone.

##The Dreaded False Negatives
On the other hand, setting the tolerance or threshold too loosely can lead to false negatives, where real defects go unnoticed. This scenario is equally problematic, as it means that visual bugs can slip through the cracks and make it into production.

Imagine deploying an application with a misaligned button or incorrect image, only to have end-users discover these issues. This not only affects the user experience but can also damage the reputation of the application and the company behind it. The relaxed threshold setting gives a false sense of security, as the tool appears to be working perfectly while critical issues remain undetected.

## The Human-in-the-Loop Problem
One of the primary goals of automated testing is to reduce the need for human intervention, allowing teams to focus on more complex and value-adding tasks. However, when using a visual testing tool with manual tolerance or thresholding, human intervention becomes inevitable.

Each time the tool flags a potential issue, someone must step in to verify whether it’s a false positive or a genuine defect. This requirement not only slows down the testing process but also introduces the potential for human error. Over time, the reliance on manual checks can lead to fatigue and inconsistency in issue resolution.

## Blinded by the Threshold
Using manual tolerance or thresholding can create a scenario where the end-user gets blinded by the settings. Instead of providing clear, actionable insights, the visual testing tool becomes a source of confusion and inefficiency. The goal of visual testing is to provide confidence that the application is visually correct, but with manual settings, this confidence is often misplaced.

## The Solution: Intelligent Visual Testing
To overcome these challenges, it's essential to adopt a visual testing tool that leverages intelligent algorithms to automatically detect and ignore minor, non-critical changes while highlighting true visual defects. Tools with smart auto-exclude features can significantly reduce the number of false positives and false negatives, ensuring that only relevant issues are flagged for review.

These advanced tools use machine learning and AI to understand the context of changes, differentiating between acceptable variations and real defects. By doing so, they eliminate the need for manual tolerance or threshold adjustments, streamlining the testing process and increasing the accuracy of results.

## AI in Visual Testing: A Game Changer
The application of AI in visual testing is one of the earliest and strongest examples of AI’s transformative power in the software testing domain. Tools like Imagium and Applitools have been at the forefront of this revolution, leveraging AI to automate and enhance visual testing processes. By using AI, these tools can intelligently identify and filter out minor, non-impactful changes, while accurately detecting real visual defects, thus significantly improving the efficiency and reliability of visual testing.

## Summary
Relying on manual tolerance or thresholding in visual testing tools is a practice fraught with challenges. It can lead to an overwhelming number of false positives, unnoticed defects, and an increased need for human intervention. Instead of simplifying the testing process, it complicates it, reducing efficiency and effectiveness.

Embracing intelligent visual testing tools that automatically handle minor variations and focus on true defects is the way forward. These tools not only save time and effort but also provide more reliable results, ensuring that your applications are visually flawless and ready for end-users. Investing in such technology can significantly enhance the quality and efficiency of your testing process, ultimately leading to better software and happier customers.

You can use tools like Imagium at no cost, which sets you free from manual configuration and saves you a lot of effort. With Imagium, you can trust that your visual testing is both efficient and accurate, giving you peace of mind and freeing up your team to focus on delivering exceptional software.