AI in QA testing -How it's changing the game.

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Testing software is hard work. You plan dozens of user scenarios, map out journeys, and update tests as the product grows. This constant evolution requires maintenance to keep tests accurate. Already sounds tedious? It certainly has been for a long time.

Market solutions have already significantly improved these processes by allowing QA testers to create and launch tests in simplified, automated ways. But what if you could improve these processes even further? Enter AI.

AI in QA testing is poised to automate tasks, boost accuracy, reduce test creation and maintenance time, and predict issues. This will free up time for strategic work—and could even earn you an extra coffee break, too.

If you're considering the future of QA enriched with AI, this article is for you.

We’ll explore the evolution of QA testing and how AI is on the verge of changing the game in the following key aspects:
  • Tests creation
  • Test Maintenance
  • Error management

Before AI until now: the evolution of QA testing

At the beginning, traditional QA testing was mostly manual. Since the early 2000s, automated testing has significantly improved the field by enhancing efficiency and reliability. Put simply: Automated testing has already made your life way easier. And AI-powered QA Testing has the potential to take it much further.

Let’s look at the table that summarizes the tech advancements in QA testing and how AI influences the whole industry.

Stage Description Pros Cons
Manual traditional

Features are tested by hand.

Relies on human intuition: essential for QA

Time-consuming and prone to human error.

Automated testing

Uses scripts and tools to automate repetitive testing tasks.

Fast: reduces manual work.

Some monitoring solutions like Kapptivate don’t require tech skills to create simple tests.

Needs technical skills to create advanced tests.

Tests can break with changes.

Testing involving AI

Uses artificial intelligence methods, such as machine learning or deep learning, to handle complex scenarios, create test cases, and adapt to changes.

Efficient: improves accuracy and drastically reduces coding and manual tasks.

Still developing: hasn’t reached maturity yet.

Now, let’s see how AI is projected to help in three key areas of QA testing: test creation, test maintenance, and error management.

AI in Test Creation

By simulating human understanding and actions, AI is making test creation faster, more reliable, and automated. Let's explore how AI is reshaping the landscape of test creation.

Accelerate Test Creation

When AI truly takes over parts of test creation, it will speed up the whole process. Let's visualize it with a fun example.

Imagine you're baking cookies. Without AI, you have to measure, mix, and shape each cookie by hand. With AI, it’s like having a master chef who adjusts everything for you—measuring, mixing, and shaping perfectly based on the ingredients available and the ideal cooking conditions. AI doesn’t just speed up the process, it learns and adapts to ensure every batch comes out just right, every time. And just like the cookie analogy, AI is set to accelerate test creation.

Here's how :
  • Rapid identification: AI can have an ability to identify elements on web interfaces.
  • Reduced errors: With fewer mistakes, tests will be ready faster.

The result? More tests created in less time with fewer headaches.

Make Test Creation More Reliable and Maintainable

Each tester has their own way of interpreting and implementing test scenarios, which can lead to inconsistencies. AI will standardize this process and reduce human bias. Relying on AI means your test creation becomes easier to maintain over time.

AI follows consistent rules and patterns, and here’s why that's beneficial:
  • Consistency: AI doesn’t get tired and applies the same logic every time.
  • Bias reduction: Removing human bias makes tests fairer.

Automate Test Creation

By placing an AI agent in front of an application or site, it will autonomously understand and execute all possible scenarios.

Here's what complete automation will involve:
  • Self-management: The AI agent will understand and interact with the interface independently.
  • Comprehensive coverage: It will test all possible user actions and paths.
  • Continuous learning: The AI will continually adapt and enhance its testing methods over time.
Autopilot sparkle

Kapptivate Autopilot

Coming soon!
Here, a description of how KTM is going to break into the market with its upcoming AI-powered Test Creation fancy feature!
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AI in Test Maintenance

With the advent of self-healing capabilities, AI is making waves in test maintenance, automating tasks that were once manual and time-consuming. It makes tests more adaptive, saving time and reducing errors.

Determine the Causes of Errors (RCA = Root Cause Analysis)

Sometimes, when a test flags an error, it turns out to be a false positive—caused by an issue within the test itself rather than the application. These false positives often happen when locators in the page’s DOM change. The tester then has to go in and fix them manually, which, as we know, can be a time-consuming task. That’s where our AI models step in. They will detect these false positives, take control of the test scenario, and automatically find the correct locators. Some common examples: the 'login' button becomes 'sign in,' a dropdown changes position, or a text element dynamically updates.

Here's how AI will do it:
  • Pattern recognition: AI algorithms can recognize patterns from vast amounts of test data.
  • Error prediction: It will also predict potential points of failure before they happen.
  • Quick diagnosis: AI can diagnose errors faster than a human, pinpointing the root cause accurately.

Very soon, this automatic understanding will speed up troubleshooting and ensure that your tests are reliable.

Automatically Update Tests According to Application/Website Versions

One of the biggest headaches in QA testing is maintaining tests as the application evolves. Changes like button relocations, new color schemes, or added pop-ups can quickly render a test obsolete. AI is stepping in to automatically keep your tests in sync with these updates.

Here's what it will do:
  • Dynamic adaptation: It will automatically adjust tests when the UI changes.
  • Smart element detection: It will identify new or relocated elements without manual updates.
  • Continuous monitoring: It will monitor application changes in real time to update tests.

For example, if a button's position changes from the top to the bottom of the screen, the AI will adjust the test script to interact with this new button placement, eliminating the need for manual updates. As a result, your tests will stay current, reducing downtime and increasing the reliability of your process.

Autopilot sparkle

Kapptivate Autopilot

Coming soon!
Here, a description of how KTM is going to break into the market with its upcoming AI-powered Self Healing fancy feature!
Interface kapptivate

AI in Error Management

AI will also help you handle error management in QA testing—you'll gain deeper insights and improve the accuracy of identifying and fixing errors.

Improve Error Interpretation

Most automated testing tools report errors in a fairly binary way. For example, a test might indicate that it failed to click on an element, simply labeling it as a failure. Kapptivate’s platform already provides detailed, visual insights into detected anomalies, but AI will allow us to take this even further.

AI Sparkle

Kapptivate Autopilot

Coming soon!
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AI will intervene here, providing detailed interpretations of errors.
  • Understanding the cause: AI will analyze why an error occurred. Whether it's due to a missing element, a timed-out script, or a broken link, AI will pinpoint the reason.
  • Precise reporting: When the issue lies within the app or site, AI will provide more precise error messages. Instead of a vague 'click failed,' you might see, 'click failed due to overlapping menu.

Detect “Side” Errors and Anomalies

AI will detect anomalies and irregularities that might otherwise go unnoticed, even when a test returns a positive result. This approach will ensure that minor issues don't escalate into major problems.

Consider these scenarios
  • Network and battery consumption: Suppose everything in the test works fine, but AI detects that the application is consuming excessive battery or network resources. This information will be critical for improving user experience.
  • Latency times: AI will monitor and report latency issues during testing, ensuring that your app runs smoothly.
Here's why this matters:
  • Unseen issues: AI will identify problems that aren't directly related to the test criteria but could affect users.
  • Preventative measures: By catching these side issues early, developers will be able to address them before they impact the end-user experience.

Using AI to detect side errors and anomalies adds an extra layer of scrutiny, making the QA process more thorough.

Sparkle AI

Kapptivate Autopilot

Coming soon!
Here, a description of how KTM is going to break into the market with its upcoming AI-powered Error Management fancy feature!
Interface kapptivate

Get ready: Our AI-driven web testing solution launches early 2025

At Kapptivate, we believe that AI has the power to transform the landscape of QA testing and synthetic monitoring. Our mission is to ensure that this transformation is meaningful, reliable, and genuinely valuable for our users. AI is not just a buzzword—it’s a tool with the potential to simplify and enhance QA workflows, making them smarter and more efficient.

We are committed to integrating AI in a way that solves real problems. We have already made significant strides, including our recent success with Eurostars project, and we're thrilled to announce that our AI-enhanced web testing solution is set to launch in early 2025.We recognize that adopting AI in QA is not without its challenges, especially when it comes to technology maturity and dependability. That’s why we take a thoughtful approach, ensuring that every AI-driven feature is robust, practical, and seamlessly integrated into our users' workflows.If you're excited about the future of AI in QA, we invite you to join us on this journey.

Drop us your email to stay updated on our progress and be among the first to experience the new wave of AI-driven QA solutions.