Manual testing remains an integral part of software development but the increasing complexity of applications demands faster and more efficient testing methodologies. This is where Artificial Intelligence (AI) comes in, offering innovative ways to enhance manual testing efforts.

AI is not here to replace manual testers; instead, it acts as a force multiplier, augmenting their capabilities, reducing repetitive work, and improving accuracy. Something that has been proven multiple times is that AI cannot test or tell the look and feel of an application as well as a human.

In this blog, we will explore how AI can augment manual testing, making the process smarter, faster, and more effective.

The Role of Manual Testing

Manual testing involves human testers executing test cases without automation tools. It is essential for:

• Usability testing – Ensuring a seamless user experience.
• Exploratory testing – Identifying edge cases and unpredictable scenarios.
• Ad-hoc testing – Finding defects that automated scripts may miss.
• Accessibility testing – Evaluating how applications accommodate diverse user needs.

While manual testing is indispensable, it also comes with some challenges like the tests being time-consuming and repetitive testing that can take a lot of effort. It is also error-prone and can also miss some defects having a defect leakage in extreme scenarios.

In addition, with all the new and evolving technologies out there manual testing is not scalable. Therefore, AI helps address these challenges by complementing human testers, allowing them to focus on more strategic tasks.

How AI Augments Manual Testing

Test Case Generation and Optimization
Creating test cases manually can be labor-intensive and inefficient. AI-driven tools can:

• Historical defect data analysis to suggest optimal test scenarios.
• Dynamic generation of test cases from application changes.
• Optimizing test coverage by identifying redundant test cases.

Intelligent Bug Detection
AI can improve defect identification by:

• Analyzing log, UI, and user behavior to detect anomalies.
• Detecting potential failure points before they occur.
• Auto-classifying bugs to prioritize critical defects.

Automated Test Execution Suggestions
AI can assist manual testers by:

• Recommending test cases based on failure probabilities.
• Identifying high-risk regions that must be tested more.
• Proposing exploratory test paths based on real user activity.

Self-Healing Test Scripts
One of the biggest pain points in automation is script maintenance. AI-powered automation tools can:

• Automatically modify test scripts when the UI or functionality is changed.
• Reduce false positives via tuning to minor changes.
• Support script learning from previous runs.

Enhanced Exploratory Testing
AI does not replace a tester but rather amplifies them. Exploratory testing still relies on a tester’s experience and intuition while AI enhances this by:

• Providing test suggestions and hints based on application behavior.
• Building real-world usage scenarios for greater testing coverage.
• Identification of probable weak areas from historical trends.

Smarter Test Data Management
AI can streamline test data creation by:

• Synthesizing test data from application requirements.
• Identification of missing test data scenarios for better coverage.
• Masking sensitive data for security and regulatory purposes.

Visual and UI Testing
Ensuring a consistent user experience across multiple devices is challenging. AI-based visual testing tools can:

• Identifies UI anomalies and layout shifts on different screen sizes.
• Identifies color contrast issues for accessibility compliance.
• Baseline screenshot comparison with new builds to highlight differences.

Predictive Analysis for Risk-Based Testing
AI can help teams focus on high-risk areas by:

• Analyzing past test run data to predict probable failure points.
• Recommending test priorities based on defect trends.
• Removing redundant tests with optimal risk coverage.

This allows testers to focus their efforts on the most impactful tests, improving efficiency.

Chatbots for Test Execution and Assistance
AI-driven chatbots can:

• Provide instant visibility into test results and defect patterns.
• Execute test cases on-demand via natural interfaces.
• Assist the author in building and optimizing test scripts.

The Future of AI-Augmented Testing, The Perfect Combination

AI is transforming the way testing is conducted, but human testers remain indispensable. It would be a great challenge for a tester to now start adapting to the new trends, just like in the past we have had many opinions about automation until we actually saw how it helped our testing. 

The future lies in:

• Human-AI Collaboration – AI handles repetitive tasks, while testers focus on critical thinking and user experience.
• More Adaptive AI Models – AI will continue to learn from test results and user behavior, improving over time.
• AI-Driven Test Orchestration – Seamless integration of AI into DevOps for continuous testing and delivery.

Artificial Intelligence (AI) is transforming software testing but it remains a hot debate among testers. While AI enhances manual testing by automating repetitive tasks, improving accuracy, and speeding up defect detection some professionals still hesitate to embrace it.

However, instead of fearing AI testers should embrace it as a powerful ally. AI eliminates tedious tasks, improves efficiency, and allows testers to focus on critical thinking and creative problem-solving.

In Summary

AI is not replacing manual testers—it is empowering them. By automating repetitive tasks, optimizing test execution, enhancing defect detection, and improving exploratory testing, AI allows testers to focus on what truly matters: ensuring a seamless user experience.

As AI continues to evolve, testers who embrace AI-driven tools will be better equipped to deliver high-quality software faster and more efficiently. The key is to strike the right balance between human expertise and AI-powered augmentation, ensuring that software testing remains intelligent, adaptive, and effective.

Are you ready to embrace AI in your testing workflows?

The post How AI Can Augment Manual Testing appeared first on AI-Powered End-to-End Testing | Applitools.

Imagine this. You built a page with CanvasJS, and you want to test the graphs. How do you create an automated test for the graphical representations? It’s testing dynamic content, after all.

This question haunts most test developers. In lots of cases, companies do lots of manual tests on the first release to make sure everything works. After that, it’s a lot of manual spot testing without automation. Because, it’s testing dynamic content, after all.

In reality, there are three approaches.

• Always do manual testing – that’s the only way to validate behavior.
• Do spot testing – trading off coverage for the cost.
• Shy away from testing and hope things work.

At the radical end of handling dynamic graphical content, some organizations decide that the job doesn’t belong to the internal web development team. These organizations farm out the entire visualization process to a third-party graphing package. For example, the Federal Trade Commission concluded that their data is best visualized using an external solution focused on data graphics and analytics. As a result, they use the services of Tableau Software to create a visual representation of the FTC data.

But, if you’re building an app for which farming out data representation might expose customer or client data, you cannot give the data to a third party and hope for the best. You have to do the visualization and own the testing of the app.  .

Dynamic Content Tests With Legacy Tools

In Chapter 4 of Raja Rao’s course, Modern Functional Test Automation thorugh Visual AI on Test Automation University, Raja walks through an example graphing app built with Canvas and asks:

“How would you test this page with dynamic content?”

He takes a bar chart example in an app using CanvasJS.

Next, Raja shows what happens when he adds a dataset to the bar chart:

What makes this problem notoriously difficult to test involves the visual nature of the behavior and the lack of handles in the DOM that correlate to the behavior. In fact, there are no links.

Opening up the Inspector for this page shows a canvas link:

In more detail, it reads:

<canvas id=”canvas” style=”display: block; width: 1233px; height: 616px;” width=”2466” height=”1232” class=”chartsjs-render-monitor”> == $0

So, all it shows is the size of the Canvas render – not the internal content. How the heck do you test this dynamic content?

With no DOM hooks, it’s impossible to know that the code above behaves as expected.

How would you handle this kind of test? When we ask, we find out that most people do is either test on occasion or not at all.   After all, if you’re using a third-party package, like CanvasJS, why not just trust it and go?

Testing Charts with Visual AI

As Raja points out, with Visual AI, you don’t need hooks in the DOM to capture app behavior. All you need to do is trigger the behavior, then capture the results visually.

Here is the test code he uses to manipulate the test chart:

View the code on Gist.

Hopefully, each step in the code reads clearly for you:

• Open the app
• Capture the screen
• Click the add dataset button
• Wait to make sure the screen executes
• Capture the screen

It seemed pretty straightforward to me when I went through it.

When you run the code, Applitools captures the tests separately as part of the same batch test run:

When you start running Applitools on these tests, the first runs get stored as the baseline expected images. You can continue to execute these tests on subsequent builds and have Applitools compare the new checkpoint against the baseline. Applitools will highlight any visual differences.

Why Does Testing Dynamic Content Matter?

In the past, I have been responsible for apps that display lots of data – like the central controller for a bunch of networking equipment. Lots of data and visualization. Each time we thought about improving the visualization, it was a huge headache. Testing alone would swallow up the QA team in apoplectic fits.

In looking at the world of visualization, there are network operations centers.

There are financial applications.

There’s even weather.

Whether you’re handling inventory, forecasting the future, scheduling appointments or doing any number of things with your applications, your customers likely will benefit from data visualizations. Why let the question of test automation limit your decision of whether or not to deploy a great visualization?

Conclusion

Everyone who deals with data needs to represent that data as more than a bunch of numbers. If you find yourself doing visual representations, you have a choice:

• Code, test, and pray
• Code, test, and spot check
• Test visually and automate tests of dynamic content.

Now that you have a way to test this dynamic content, what’s stopping you?

For More Information

• Sign up for Test Automation University
• Read all about Functional Test Myopia
• Modernize Your Functional Testing– Chapter 1 of Raja’s course.
• Advanced Tools for Testing Tables – Chapter 2 of Raja’s course.
• Data-Driven Testing with Visual AI – Chapter 3 of Raja’s course.
• Visit the Automated Visual Testing Course on Test Automation University
• Blog: How Do I Validate Visually?
• Sign up for a free Applitools account
• Request an Applitools Demo
• Visit the Applitools Tutorials

The post How Do You Test Dynamic Content? appeared first on AI-Powered End-to-End Testing | Applitools.

In the world of software testing you have challenges coming at you from all angles: more functionality to test in increasingly complex and larger applications, pressure from management to find issues faster, shorter release cycles, and possibly shrinking teams. The pressures can be so intense that something has to be sacrificed, and what is sacrificed is usually the depth and breadth of your testing.

You know that automation can help, but introducing automation into a manual testing environment is not easy. You cannot just stop everything and introduce new tools. You have to get special talent, and you essentially have to start over.
What you really need is the ability to improve the efficiency of manual testing, with little to no effort. 

Seriously, You Expect Me to *Increase* Coverage?

Yep. Without increasing test coverage you can’t keep up with the increasing pressures. But more importantly, you should have the goal to enter the land of exploratory testing. That is where the entire software delivery pipeline will benefit from the test results, versus just production.

Wait… What? Visual Testing?

One really cool invention that helps you increase both depth and breadth of manual testing with little effort is visual testing (AKA perceptual diff). Basically, Visual Testing technologies compare screenshots of the application to screenshots of previous versions. It allows you to test different aspects of your application, for example: testing if an image moved, or if changes are displayed correctly on the page.

So Why is Visual Testing Good for Me?

Well, there are 3 main areas where visual testing really makes a difference, especially when you need to release quickly without visual regressions (and these days, who doesn’t?):

Visual testing increases accuracy. A good visual testing solution can detect even the smallest of UI issues, and will not let any visual bug sneak under the radar, or escape the watchful eye of the tester. Customers of such advanced solutions report catching 50% more visual bugs pre-release.

Visual testing increases testing speed. With visual testing, testers are not bothered with manually navigating and scanning pages looking for UI issues, which is tedious and prone to human error. Instead, they will have a dashboard of all screenshots, and the variations between them. This automatically points them to potential problems and allows them to make educated and more accurate decisions about the highlighted differences.

Visual Testing solutions use powerful algorithms, therefore can scan an entire page in under a second – and a manual tester will need another minute to assess the results only if a difference was noted. Just to make sense of it: If it takes about 10 minutes to manually test a typical web page, with Visual Testing, page test time goes down X10 (from 10 minutes to 1 minute). When you apply these numbers across multiple apps, websites, pages, and versions – you can cut down manual testing time from weeks to hours.

Another huge benefit is the ability to use Visual Testing when you do exploratory testing and thus increase test coverage. The reason coverage is increased with visual testing is because visual tests capture everything on the screen from the user’s perspective. That means that in a single view they capture all the UI features and fields on one page, and as more application pages are captured, functionality across your entire application is properly tested.

Tools of the Trade

There are several solutions and tools that address Visual Testing, from basic bitmap comparison tools (that offer standard differences between images), towards high-end solutions with advanced image processing algorithms. These differences account for different levels of performance when it comes to visual validation and testing:

• Image-based functional testing, such as Sikuli and Eggplant

• Open source visual testing with basic image comparison, such as WebdriverCSS and Wraith

• Content Analysis, combined with Layout Analysis (i.e. page structure) and Style Analysis (e.g. font, color, etc.), such as Applitools Eyes. The combination of these capabilities gives you robust testing with minimum false positives and makes manual testing much faster and more accurate.

But these tools require automation, or at least some coding know-how, so how can you still augment your manual testing efforts to the speed of automation? 1-Click Visual Testing Tool that requires no test code could be a great way to start.

1-Click Visual Testing: 100% Accuracy, 0% Test Code

Applitools Eyes Express offers manual testers a great way to increase speed, coverage, and accuracy – without the need to write test code. This simple-to-use browser extension allows you to validate your entire site’s UI in a single click – and get immediate results in seconds, helping you avoid UI bugs and visual regressions.

Our scriptless 1-click visual testing solution was specifically developed for our fast-pace-continuous-delivery environment, allowing manual testers to test more and test better in less time, not to mention it’s more robust and offers more capabilities than any other visual comparison tool available today – without the hassle of coding or lengthy implementation.

Bottom Line

It is impossible to implement full automation overnight. It’s a long process that involves organizational & personal learning, investment in the proper tools, and definitions of new work processes. But by using Visual Testing technology – either to complement test automation, or as a stand-alone 1-click-zero-coding visual testing tool – you can substantially speed up your manual testing, and alleviate some of the pressures received from management, development, and operations.

Watch this 2-minute demo video about 1-click visual testing:

To read more about Applitools’ visual UI testing and Application Visual Management (AVM) solutions, check out the resources section on the Applitools website. To get started with Applitools, request a demo or sign up for a free Applitools account.

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