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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.
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.
Now, let’s see how AI is projected to help in three key areas of QA testing: test creation, test maintenance, and error management.
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.
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.
The result? More tests created in less time with fewer headaches.
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.
By placing an AI agent in front of an application or site, it will autonomously understand and execute all possible scenarios.
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.
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.
Very soon, this automatic understanding will speed up troubleshooting and ensure that your tests are reliable.
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.
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.
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.
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 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.
Using AI to detect side errors and anomalies adds an extra layer of scrutiny, making the QA process more thorough.
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.
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