What is the use of AI in software testing?
AI in software testing means using artificial intelligence algorithms to automate and optimise different phases of the testing process. The goal is to offload as many repetitive processes as you can and execute them faster, smarter, and more efficiently.Ā
AI can come in handy in QA and software testing in different ways, which we will discuss further and in detail. But for now, you need to know several things about AI in testing:
- AI is not a magic solution that eliminates all manual effort
- Despite its reputation, high-quality AI can supercharge your efforts and save you up to 98% of time in different stages of software testing
- AI is not a set-it-and-forget-it solution also, you still need maintenance and adjustmentsĀ
- With proper use of AI, you will cut a lot of costs and free up a lot of resources in your team, which you can spend on many crucial matters
So let’s separate facts from myths in QA, there are no extremes: AI is not replacing testers, it just helps them with redundant, repetitive tasks that can be automated.
How is AI used in testing?
A few years ago, the connection between AI and software testing was mostly about basic automationāfor example, simple test scripts running predefined actions. It helped speed up regression testing but lacked some qualities too: it couldn’t adapt to changes or make smart decisions.
After the rise of GPT-based solutions and advanced machine learning, AI started doing more than just automation. It could now analyse test data, identify patterns, and even predict defects before they happened. Tools began using AI to self-heal broken test scripts, generate test cases from requirements, and optimise test coverage with minimal effort. It is no surprise that after ChatGPT became popular, a popular study revealed that at least 78% of software testers use AI in some way in their workflows.
Now the possibilities are almost endless. Letās look at the main ways AI can transform your testing efforts.
Applications of AI in software testing in 2025
You can use AI in different phases of the software development lifecycle (SDLC). You can also look at this section as a list of benefits since we explain what AI saves you in each category and exactly what you offload from your QA efforts. Here are the main applications of AI software testing:
1. Requirements creation
A proper business idea should be formulated as a requirement. On the other hand, in QA, writing comprehensive test cases also starts with having clear requirements. So, let’s start with this stage.
Unclear or incomplete requirements often cause a lot of problems. On the other side, you need to be fast and efficient at the same time. That is where you use the power of AI to deliver perfect requirements for complete test cases. And the good news is that it could be as close as just saying a few words or creating brief notes. How?Ā
Example: An AI-powered test management system (TMS) aqua cloud helps you create requirements easier than ever before. With aquaās AI, you just need to speak a few words or present brief notes – they do not need to be super-organised – and in one click, your requirement is ready.
2. Test case creation - the era of AI test case generators
If you were a tester before the rise of AI, the current situation would definitely make you jealous: what took several minutes, sometimes half an hour can be done in seconds now. In the past, you needed to write test cases manually and sometimes this process would consume days of your time. Letās mix the human factor of being inattentive or procrastinating, and you have a recipe for a major problem: time-consuming, non-reliable, and worst of all – repetitive process. Now AI handles it easily.Ā
We can see that there are still a lot of complaints across the web on high-quality AI test case generators. For example, some Reddit users gave their opinions on the matter like this:
We were previewing some different test case management tools and one had an AI test case generator. Lets just say it was not good.
Many AI test case generators fail because they produce vague, overly generic, or irrelevant test cases that lack proper structure. They miss critical edge cases, repeat unnecessary steps, or generate outputs that still require heavy manual revisions. It literally defeats the purpose of automation. Thatās why some testers are frustrated.
However, the best AI-powered test case generators take a different approach. They analyse real user scenarios, application-specific workflows, and historical defect patterns to generate detailed, structured, and actionable test cases. They donāt generate steps randomlyāthey follow industry standards, adapt to project requirements, and ensure tests align with real-world conditions. This means less manual cleanup, higher test coverage, and ultimately, a faster QA process.
But how much higher and faster? Now it is time to clear the fog in the matter: AI test case generators can replace your manual test creation easily, with much faster and high-quality results.Ā
Example: Letās say you need to create a 450-word, 15-step test case. Manually, it takes around 3 seconds per word and 30 seconds to plan each step. Even if you stay fully focused, this means:
- 450 words Ć 3 sec = 1,350 sec (22.5 min)
- 15 steps Ć 30 sec = 450 sec (7.5 min)
- Total: 30 minutes for a single test case
aqua cloudās AI test case generator can create the same test case in seconds, with one click, saving you 30 minutes per case. Over time, this adds up to hours of saved effort per day.
aquaās test case generator lets you focus on more valuable tasks like exploratory testing and defect analysis instead of repetitive typing. Why not eliminate the boring stuff once and for all?
3. Test Data Management with AI - AI test data generators put you a step ahead
We all know this: good test data is hard to get. You either use real user data (risking privacy issues) or manually create mock data (which is slow and unrealistic). AI analyses real-world patterns and helps you generate synthetic yet realistic data. This way, your tests cover a wide range of scenarios, much wider than with manual mock data creation.
Example: aqua cloudās AI can create thousands of rows of test data for testing a wide range of systems without using real customer data. This ensures compliance with privacy regulations like GDPR while still allowing meaningful tests.
With aqua cloud, all you need to do is click on a test case item and click on āgenerate test data with AIā:
As you can see, aqua cloud helps you generate requirements, test cases, and test data in just 3 clicks. It is the first solution that brought the power of AI into software testing and has evolved ever since. Using aqua cloud means saving 97% of your time in the test planning stage. Additionally, all these are at your service: integrations with solutions like Jira, Azure DevOps, Selenium, a centralised repository for all your automated and manual tests, GDPR compliance, customisable reports, full control over your test management, 100% traceability, test coverage, and much more. Choose aqua cloud and eliminate manual, redundant and repetitive.
Change your software testing effort for good with a 100% AI-powered solution
4. Automated Test Script Maintenance and Self-Healing
UI changes can be a nightmare for testers. Whenever the UI changesāwhether it’s a button position, color, or layoutātraditional test automation often breaks down. It requires testers to manually update locators or adjust scripts. This can be a huge time drain.
AI solves this issue in two ways: automated test script maintenance and self-healing. Both AI techniques automatically detect changes and adjust test scripts, but self-healing takes it a step further. It ensures tests continue to run smoothly without failure, even when unexpected changes occur.
Example: Applitools offers AI-powered visual testing that detects UI changes dynamically. If the “Buy Now” button moves slightly, AI updates the locator, and the test continues. Self-healing takes it even further by ensuring that changes are handled in real-time, keeping your tests stable even with frequent UI updates.
5. Defect Prediction and Analysis ā Finding Bugs Before They Happen
One of the superpowers of AI is it can analyse past bug reports, test logs, and production issues to predict where defects are most likely to occur. Instead of testing everything equally, you can focus on high-risk areas.
Example: Now tools like Defect AI analyse historical defect data to predict where failures are likely. If a banking app’s past releases show that login failures are a common issue, AI suggests running more tests on authentication flows before a new update.
6. Regression Testing Optimisation ā Running Only What Matters
Running a full regression suite takes forever. AI analyses code changes, chooses only the most relevant tests, and skips redundant ones. This speeds up testing while maintaining high coverage.
Example: Tools like Rainforest QA offer AI services specifically for regression testing automation.Ā
A popular study found that AI-powered regression testing reduces test execution time by 40% by selecting only the tests that matter. This lets you focus on exploratory testing instead of waiting for unnecessary tests to finish.
What are the 3 categories of AI in software testing?
So AI role in software testing can be categorised like this:
1. Generative AI ā Creating & Enhancing Tests
AI that generates test cases, test data, and requirements based on historical data and application logic. The prime example is aqua cloud.Ā
2. Self-Healing & Predictive AI ā Fixing & Optimising Tests
AI that fixes broken scripts, adapts to UI changes and predicts defects before they happen. Defect AI and its services are great examples.Ā
3. Execution & Analysis AI ā Automating & Improving Testing
AI that optimises test execution, automates cross-platform tests and provides advanced reporting. A fitting example – Applitools.Ā
So, does AI mean all sunshine and rainbows? No, and using AI comes with its challenges. Letās look at them one by one.
Challenges of implementing AI in QA
Bringing AI into your QA processes can be both beneficial and problematic. We discussed the benefits in the above section, so now itās time to face some realities. Below are the challenges youāll face using AI (especially for a long time) in QA:
1. ComplexityĀ
AI is āblack boxā – which means the inner workings could be unclear even for its creators. So using it frequently will definitely give faulty or problematic outputs (at least once in a while). This is one of the guaranteed challenges with AI – you will face it using any AI-powered tool. And it should not discourage you from using AI in QA, because no AI model in the world is perfect.Ā
Solution: You need to go for solutions with at least some transparency, Forbes article says. In QA, it translates into traceability so you can see where you get the results- which requirement leads to which test case.
Letās look at a specific example to see the problem and how you can deal with it.Ā
Example: Letās imagine you use an AI-powered test case generation tool.Ā AI analyses requirements and produces a set of automated test cases. However, one of the test cases includes an unexpected validation – no one on the team added this manually. You are not sure why the AI inserted it, and canāt verify if it’s a bug or a necessary test step.
How aqua cloud helps in this case: aqua provides full traceability and history tracking, so you can see how AI-assisted test generation arrived at specific steps. It doesnāt leave you guessingāit keeps AI decisions transparent, so you stay in control.
Enjoy 100% traceability with AI to tackle all complexity and achieve full clarity
2. Trust in AI modelsĀ
When you rely on AI to carry out any of your tasks, there is something in your heart that says āDouble check, because it can all fall apartā. That is because sometimes AI models are not trustworthy at all. No matter how great you are in your prompt creation or how accurate the data you feed to AI is, the results can still be faulty.
This mainly depends on the AI model you are working for, but another factor is how open you are to inevitable changes. For example, a popular KPMG study shows that 67% of people report low to moderate acceptance of AI. This varies in different fields. What about QA?Ā
Although 78% of testers use some form of AI in their QA practices, the opinions on social media platforms vary. Letās look at two other quotes from Reddit users:
Am I the only one who's tired of all this AI hype and just wants to actually do my job?
From everything I've seen with AI, it certainly seems like manual QA is needed more than ever. But for how long?
We understand, you still want to do high-quality job and not fall behind this AI hype. The approach we suggest will help you with that:
Solution:
- AI as a helper, not the decision-maker ā Automate tasks, but always verify results.
- Choose tools with transparency ā Look for AI that explains its decisions, not just gives results.
- Run AI alongside traditional testing ā Cross-check AI findings with manual or scripted tests.
- Feed AI high-quality data ā Avoid generic datasets; train AI with relevant, project-specific cases.
- Pick AI that adapts ā Use tools that self-heal, learn from failures, and reduce flaky tests over time.
AI wonāt replace testersātesters who use AI will replace those who donāt.
3. Security & Privacy concerns
In QA, you mostly work on confidential projects with sensitive data. Then all of a sudden for this reason or another, you rely on AI with this sensitive data. What can go wrong, right?Ā
Everything. There are several cases where it went disastrously wrong:Ā
- 2023 Samsung ban on ChatGPT:Ā Sensitive internal source code was uploaded by an engineer to ChatGPT and it got leaked. It turns out these chatbots use sensitive info, store them and might even use them in their future answers to OTHER PEOPLE, outside your organisation. Samsung was not the first, and definitely wonāt be the last company to ban their employees from using AI.Ā
- 2020 Clearview AI disaster: Clearview AI scrapped billions of photos from social media without permission. In 2020, hackers stole its client list, exposing over 2,200 companies.
- Recent Deepseek chaos: After gaining huge popularity in recent weeks, Deepseek also messed up: recently more than 1 million sensitive records were leaked and anyone with an internet connection could access them.Ā
It raises moral, technical, and sensitivity concerns about AI for QA engineers. In software testing, it matters even more, because you work with sensitive data all the time.
Solution: When it comes to security and privacy concerns in AI-powered testing, aqua cloud has you covered. With its adherence to industry standards like SOC2, ISO 27001, and GDPR, aqua handles your data securely and in line with best practices, so you donāt have the risk of data breaches. With continuous monitoring capabilities you can detect and respond to security incidents in real-time and minimise the impact of any potential vulnerabilities. If you’re working in highly regulated sectors like banking or government, aqua’s secure environment offers the peace of mind you need to focus on quality, not on the threat of data leaks.
Rely on a 100% AI-powered QA solution with all your sensitive data
4. Lack of Skilled Employees (with AI)
AI tools require specialised skills to properly integrate and manage. As AI is still relatively new in the QA space, itās not uncommon if you struggle to find testers who fully understand how to implement, monitor, and optimize AI tools. Without the right expertise, AI solutions can quickly become underused or misused. Overused means you will need more manual tweaks and lose time. Underused means you will lose a lot of potential on money and time-saving.
Solution:
- Training and Upskilling ā Equip your team with the necessary skills through training programs or certifications.
- Choose User-Friendly Tools ā Look for AI-powered tools with intuitive interfaces and easy-to-use features, like aqua cloud. It requires minimal technical expertise while offering maximum benefit.
5. AI Over-reliance: Letting AI Do It All
Another common problem is the over-reliance on AI. While AI is incredibly useful, it’s not infallible, and letting it take over all tasksāespecially in complex and dynamic environmentsāwill lead to more problems. For instance, AI models often struggle with ambiguous or new types of issues that it hasnāt seen before. As a result, either bugs will slip through or releases will be delayed, and your team canāt afford both.
Solution:
- Balance AI with Humans ā Let AI handle the boring and low-risk tasks, but leave tough decisions and problem-solving to yourself.
- Use AI to Help, Not Replace ā Let AI assist with test cases and reporting, while you focus on planning and evaluating.
We have covered almost all theory you need. Letās go to the list of solutions now.
Best AI software testing tools of 2025
We will divide these into subcategories, so find exactly what you need:
1. Best AI Test Case Generators
- aqua cloud ā aqua uses AI to auto-generate test cases based on requirements in just one click. It creates tests 98% faster and with better coverage than you can on your own. aqua delivers 100% traceability and aligns the test cases with project requirements for full transparency.
Create a complete test case from requirement in 10 seconds
- Testim.io ā Testim also uses AI to automate test creation and maintenance. It speeds up the process and is among the top tools for UX/UI testing, but it sometimes struggles with handling complex UI changes effectively.
- Functionize ā Functionise uses AI to generate and run tests, making it easier to test complex applications. However, it can be slow when adapting to significant UI changes.
- mabl ā mabl automates test case creation and integrates with CI/CD pipelines. It’s powerful, but sometimes its AI struggles with very dynamic and complex test cases, also users have complained about edge case coverage.
2. Best AI Requirements Management Tools
- aqua cloud ā with aqua, you turn your brief notes, voice prompt, image, or any other format into a complete user story, BDD or PRD in seconds. This AI-powered solution gives you full visibility into how your requirements lead to test outcomes.
Requirements management is down to a few seconds with AI in 2025 - give it a try
- IBM Engineering Requirements Management DOORS Next ā This tool offers AI-powered management features for large-scale projects, but it is not considered for smaller teams due to its complexity and prices.
- Jama Connect ā Jama Connect uses AI to help manage requirements, traceability, and testing. However, there is a steep learning curve and non-availability for smaller teams again.
- Helix RM ā Helix RM offers AI-powered tracking and management of requirements. It offers great traceability and integration, but it can be complex to set up initially.
3. Best AI Self-Healing Tools
- Testim.io ā Testimās self-healing mechanism automatically adapts to changes in the application and it reduces maintenance work. The main concern is that the process is sometimes slow in identifying complex UI changes.
- Functionize ā Functionize’s self-healing AI detects and fixes tests as the UI changes, offering great value for automation.Ā
- Autify ā Autify offers self-healing capabilities for automation, which helps keep your tests running smoothly even when there are frequent UI changes. There are some concerns about the flexibility and adaptability of the tool.
- Sauce Labs ā Sauce Labs offers self-healing functionality for automated tests. Especially in highly dynamic applications, whether it handles significant UI changes effectively is a mystery – there are reviews for both.
4. Best AI for Testing Automation
- Applitools ā Applitools offers AI-powered visual testing that detects visual bugs and differences across browsers. It’s reliably accurate but can be more suited to visual testing than functional testing.
- mabl ā mabl offers AI-driven software testing and automation that integrates with CI/CD pipelines. The users sometimes struggle with scalability in larger systems.
- LambdaTest ā LambdaTest uses AI to run parallel tests across multiple browsers and devices and improves test execution speed. However, its AI requires more refinement for complex application scenarios. Overall, you can consider LambdaTest an option for end-to-end testing.
- Selenium (with AI integrations) ā Selenium, when paired with AI integrations, provides powerful test automation, but it lacks out-of-the-box AI-powered features without additional setups and tools.
5. Best AI tools for Bug Tracking and Management
aqua cloud ā With aqua, defect reporting becomes a breeze. By recording every interaction with just one click, aquaās native integration Capture automatically generates time-stamped videos and environment details in seconds. Customisable defect templates allow for quick input, and workflows guarantee that defects are handled by the right people. You can easily attach documents, videos, and screenshots to provide a clear and thorough explanation of issues. Plus, during test execution, aqua cloud auto-fills expected and actual results, saving you valuable time, energy and effort.
Save 6.5 hours/week on bug reporting
- Bugsee ā Bugsee uses AI to help track bugs and provide context about how they occurred.
- YouTrack ā YouTrack uses AI to assist in managing bugs and issues and offers automatic categorisation and prioritisation. However, it lacks the deep AI-powered integrations of other tools.
- Redmine (with AI integrations) ā Redmine, when paired with AI-based plugins, offers useful bug-tracking features.
6. Best AI for Software Testing Analytics and Reporting
- QA Touch ā QA Touch uses AI to provide actionable insights into your test results and performance metrics. Its reporting features are solid, but the tool could use more advanced predictive analytics.
- mabl ā mabl provides real-time analytics and insights into test execution, but its AI-driven reporting can sometimes be overwhelming for new users.
- Allure Framework (with AI integrations) ā Allure provides insightful reporting, and when paired with AI, it helps you uncover patterns in test data. The main concern is it is less intuitive for newcomers.
Based on your needs, you can jump back to a category and choose the top tool for your pain points in QA in 2025.
Conclusion
So what final thoughts can we derive from all these? First, AI will take over, sooner, or later. Second, AI does not replace testers, testers using AI will replace the ones who donāt. Everyone wants efficiency in their workplace, so if you are a part of any QA team, make sure you bring maximum efficiency (with AI-based testing) to that team. Third, soon it will be much harder to decide on what solution to choose for fast, efficient and reliable results (aqua cloud winks at you at the moment). Choose your AI tool for software testing wisely, and do not forget to add the crucial, necessary human touch to avoid the disasters we discussed in this article.
