Intelligent testing: 14 AI tools for test automation

AI will continue to change many industries in 2025, and software testing is no exception. Just a few years ago, AI/ML features in testing tools were limited to advanced software. Now, it's commonly considered as a must-have functionality. In this article, we delve into the top test automation tools enhanced with AI. Below, we share insights about both regular test automation software and AI-enhanced ones, the key features AI brings to testing, and our selection of the top AI test automation tools.

What are AI testing tools?

AI testing tools automate and optimize the testing process using core AI capabilities — machine learning, natural language processing, and predictive analytics. They are a crucial part of software development, as they make traditional testing methods more efficient:

• Less manual work for testers — minimal or no involvement;
• Faster error detection for fixing;
• Comprehensive and proper software quality checks, regardless of scale.

Testers can use such testing tools to conduct various types of testing, including functionality, performance, security, and even UI/UX testing.

How do AI testing tools work?

AI testing tools simulate human decision-making during the testing process. Let’s try to break it down as simply as possible.

Data Analysis

A software testing tool analyzes huge amounts of historical data to find patterns, trends, and common areas where errors occur. It allows the top AI testing platform to know which part of the software to test first.

Test Case Generation

AI test automation tools can generate test cases based on application code and user behavior. This reduces the time spent on manual testing and covers many more test scenarios.

Model Self-Learning

Over time, AI testing tools get better by learning from previous automated tests during software development. They adapt to changes and get better at finding errors and performance issues.

Automatization

One of the biggest benefits of AI testing tools is the automation of repetitive tasks. So, the testing process can be done continuously without human intervention.

Why do we need AI testing tools?

• Automate the boring stuff. Testing tools eliminate the need for specialists to run the same tests repeatedly during manual testing and monitoring so they can focus on more complex and skilled work.
• Increase accuracy. Machine learning can analyze failed and successful tests to predict where problems will occur.
• More test coverage. A dedicated automation platform can create many test cases that would be missed with traditional tools.
• Test faster. AI testing tools cut down test time so you can meet your software development deadlines.
• Agile and DevOps. AI test automation is perfect for places where continuous testing and fast feedback matter.

Regular test automation tool VS AI-powered software

The main difference between a regular test automation tool and an AI-powered one lies in the underlying technology and capabilities it leverages.

Automation approach

Regular test automation tool. It typically relies on predefined scripts and rules created by testers or developers. The automation script follows instructions to interact with the application and verify expected behavior.

AI-powered test automation tool. Utilizes AI and ML algorithms to enhance automation. It may employ techniques such as pattern recognition, self-learning, and adaptability to changes in the application.

Scripting and maintenance

Regular test automation tool. Requires manual scripting for test cases. Maintenance can be time-consuming, especially when there are changes in the application's UI or functionality.

AI-powered test automation tool. Can adapt to changes in the application more dynamically. Some AI tools can self-heal, automatically adjusting test scripts when the application undergoes modifications.

Test data handling

Regular test automation tool. Relies on predefined test data and may require extensive data management efforts for different scenarios.

AI-powered test automation tool. Can analyze and generate test data based on patterns and historical information. It may have the capability to understand and generate relevant test scenarios.

Object recognition

Regular test automation tool. Typically uses explicit locators to identify and interact with UI elements. Changes in the UI structure may require manual adjustments to the scripts.

AI-powered test automation tool. Utilizes advanced image recognition and natural language processing to identify UI elements. It may be more resilient to changes in the application's visual elements.

Adaptability

Regular test automation tool. May require constant updates and modifications to scripts as the application evolves.

AI-powered test automation tool. Can adapt to changes more autonomously, reducing the need for frequent script adjustments.

Test case generation

Regular test automation tool. Test cases need to be manually created by testers based on requirements.

AI-powered test automation tool. Can assist in generating test cases by analyzing application behavior and learning from user interactions.

Complexity of test scenarios

Regular test automation tool. Well-suited for straightforward test scenarios with clear rules and expected outcomes.

‍AI-powered test automation tool. Excels in handling complex scenarios where there are variations, uncertainties, or a high degree of interactivity.

So, the benefits of AI testing tools are…

… speed

AI testing tools test faster than manual testers.

… cost

AI capabilities automate repetitive tasks and save companies money in the long run by not needing manual testers.

… accuracy

AI software testing can detect patterns and trends humans miss, so there are fewer false positives and missed bugs.

… coverage

AI automation testing tools can generate and run thousands of test cases to catch even the weirdest bugs.

... flexibility

As the code changes, test automation platforms learn.

AI testing tools possible features

Test script generation. AI testing tools revolutionize the test scripting process. Through advanced algorithms and machine learning capabilities, these tools can automatically generate test scripts, significantly reducing the manual effort required in the scripting phase. By analyzing application behavior, AI testing tools assist in creating robust and comprehensive test scenarios.

Intelligent test execution. AI testing tools bring intelligence to the test execution phase. They can dynamically adapt test execution based on real-time conditions, making decisions on the fly. This adaptability ensures efficient test runs by focusing on areas that require attention, optimizing testing resources, and providing quicker feedback to development teams.

Smarter test maintenance. One of the challenges in test automation is maintaining scripts when the application undergoes changes. AI testing tools address this issue with smarter test maintenance capabilities. They can automatically update test scripts to accommodate changes in the application's UI or functionality, reducing the manual effort involved in script maintenance.

Root cause analysis. When tests fail, AI testing tools go beyond traditional reporting. They incorporate root cause analysis capabilities, helping testers and developers pinpoint the exact source of issues. By analyzing logs, historical data, and test results, these tools provide insights into the underlying causes of failures, facilitating faster and more accurate issue resolution.

How to choose AI testing tools?

Here are some simple tips for choosing the right AI testing tool. Consider these factors to get what you expect!

• Ease of use. Look for tools with a user-friendly interface and no need for extensive programming knowledge. Ideally, features should allow you to integrate them into your existing workflows without hassle.
• Compatibility. Make sure the AI testing tool is compatible with your technology. Whether it’s web-based software, mobile apps, or desktop apps, the tool should meet your specific requirements.
• Scalability. Are you working on a growing project? Use a tool that can scale with your needs. Ensure it can handle growing data volumes and provide enough test scenarios.
• Cost. Evaluate the pricing model and make an objective decision if the testing capabilities justify the cost. Some tools offer free trials or multi-level pricing that can help you try the tool before buying and integrating it into your workflow.
• Community and support. A strong user community or good customer support also matters. You’ll likely need help in the future to troubleshoot or implement new features, especially as the tools evolve.
• Confidentiality and data security. Since AI testing tools work directly with data, make sure the specific tool meets strict confidentiality, data security standards, and your project requirements.

Top 14 testing tools with AI components description

AI testing tools have changed the way we approach automation, test efficiency, and quality assurance. Here are 14 of them that can help you tackle your daily tasks.

Zebrunner is an advanced testing tool with functionality for both test management and automation reporting. Zebrunner’s distinctive features include a focus on AI capabilities (such as AI-driven bug classification and AI-generated test case suggestions) and an easy-to-use interface.

As seen in the screenshots, the tag "business issue" is assigned by AI/ML. Clicking on the exclamation mark provides additional information, including an accuracy percentage and the requirement for manual review.

The focus of Zebrunner's AI/ML technology is on detecting potential regression bugs, particularly those arising after the introduction of new features. The model learns rapidly, simplifying the work of automation engineers by swiftly determining the cause of failures without the need for extensive analysis. A notable feature is Zebrunner's AI/ML technology prioritizing potential bugs in the first position. Results are based on qualitative indicators, ensuring a focus on quality over quantity and providing valuable insights for efficient bug resolution.

Another important AI functionality, the AI autocomplete feature, simplifies and speeds up test case creation by enabling autocompletion for partially filled cases using generative AI. The AI-generated test cases for a test suite feature enhances the suite by generating new test cases based on suite details and existing cases. Users can easily adjust suggestions before saving them to suit their preferences. Additionally, users can now generate test cases based on the content of Jira and Azure DevOps tickets.

Key features:

• Generative AI for test case suggestions. Zebrunner's generative AI suggests new test scenarios based on existing test suite details, enabling QA teams to quickly generate and customize relevant test cases.
• AI/ML-powered root cause analysis and categorization. Zebrunner leverages AI/ML for automated root cause analysis, categorizing failures, and identifying potential regression bugs, especially after new feature releases. This prioritizes critical issues and accelerates bug resolution.
• Autocomplete for test case fields. With an Autocomplete feature for partially filled test cases, Zebrunner completes empty fields by analyzing similar cases, ensuring consistent and efficient test case documentation.
• Seamless third-party integration. Zebrunner supports smooth test migration from other tools and integrates well with Jira and other popular QA tools, enhancing collaboration across QA teams.
• Advanced customization options. With features like shared steps, restorable test cases, requirement and defect linking, and custom fields, Zebrunner allows tailored test case management to fit specific project needs.

Mabl’s AI tool is for continuous testing in agile teams. It has integrated machine learning for functional and performance testing.

Mabl is a low-code test automation solution with native auto-healing capabilities, ensuring tests adapt to UI changes for improved reliability without manual intervention. Another AI feature utilizes historical performance data to optimize test timing, reducing failures. Additionally, the software employs machine learning to cluster similar application URLs, offering insights for prioritizing tests effectively.

Key features:

• Self-healing tests. Mabl’s AI engine detects changes in the UI and updates test cases.
• Smart element identification. The tool finds elements across browsers and devices.
• Easy CI/CD integration. Mabl integrates with popular CI/CD pipelines for continuous and fast feedback.

Perfecto is a platform for testing web and mobile applications. The use of AI and ML simplifies result interpretation, allowing teams to focus on real issues with the help of AI filters.

Key features:

• AI-powered test automation. Self-healing technology updates tests when the UI changes.
• Seamless integration. It integrates with popular CI/CD tools for continuous testing.
• Scriptless testing. Non-technical testers can create complex tests without coding.

Testsigma is a cloud-based test automation tool that uses AI to make test creation and maintenance easy. It’s ideal for teams that need to test across multiple platforms.

This automated testing platform enables testing for web, mobile, desktop apps, and APIs in one tool. AI takes maintenance off manual control by automatically fixing broken scripts, healing dynamically changing elements, and proposing solutions for test failures. An additional feature is NLP-based scripting that enables users to write automated tests in English. The Record-and-playback feature lets users generate test scripts through Testsigma Recorder.

Key features:

• Natural language test creation. The AI testing tool creates test cases in plain English. Non-technical specialists can successfully use it.
• AI maintenance. Detects changes in the application and updates tests automatically.
• Multi-platform testing. It supports web, mobile, API, and desktop testing on one platform.

Testim is an end-to-end AI testing platform that creates stable tests fast. Great for teams that want to scale their test automation and keep quality high.

Testim.io is an AI test automation tool designed for web and mobile testing. The software eliminates costly maintenance through AI and ML smart locators. These locators comprehend your app, secure elements, and autonomously enhance to provide unparalleled stability, minimizing maintenance. Generative AI is employed to diagnose and resolve failures.

Key features:

• Self-healing tests. The tool rebuilds tests when the UI changes, hence no extra maintenance costs.
• Fast test creation. AI creates test cases in no time, and deployment is fast no matter what.
• Cross-platform. Works on web, mobile, and desktop applications.

Aqua is a test management system for test cases, defects, requirements, and test automation, enhanced by AI. The website of this software product promises to make testing faster than humanly possible. The Aqua AI model understands the context and semantics, and it can be used to turn speech into requirements, generate test cases, find patterns, and more.

AccelQ is an AI-powered test automation platform. It serves as a platform for achieving continuous test automation, focusing on end-to-end business assurance, zero coding complexity, packaged apps automation, and quality lifecycle management. This singular platform seamlessly connects to all testing needs, working across diverse application portfolios, including Web, Mobile, API, and Desktop.

Testing activities Applitools focus on the app's appearance across devices and browsers.

Applitools is a test automation platform that leverages the power of Visual AI. This software promises to streamline the creation, execution, and maintenance of automated tests by replacing traditional functional testing with artificial intelligence. Through this innovative approach, Applitools minimizes manual efforts throughout the testing process.

The platform offers several key features, including codeless test creation, maintenance, execution, and reporting within a unified platform. It incorporates visual AI validation, enabling the automation of UI testing at an unprecedented scale. Applitools also boasts a self-healing test infrastructure that ensures the resilience of tests using OS frameworks. Additionally, the platform facilitates rapid testing across various browsers and devices, allowing for massive scalability.

Sauce Labs is a comprehensive toolset for website and mobile testing throughout the entire development process. Sauce Low-Code empowers test automation engineers to simplify test case creation and execution through AI-driven SaaS testing. Sauce Labs is particularly effective for cross-browser, mobile, and API testing.

TestComplete is a powerful UI testing automation tool that stands out for its AI-powered object recognition engine and versatile scripting options. With this tool, you can effortlessly test desktop, web, and mobile applications. TestComplete features an intelligent object repository and supports over 500 controls, ensuring that your GUI tests are scalable, robust, and easy to manage. By leveraging AI-powered capabilities such as automated visual testing, and self-healing tests you can simplify workflows, spend less time on test maintenance, and focus more on tasks that drive progress. 

Key features:

• Object recognition engine. AI powered object recognition to identify elements even if their attributes change.
• Scriptless automation. Users can carry out codeless test automation thanks to an intuitive interface.
• Parallel testing. AI testing tool runs multiple tests at once, faster, without compromising quality.

Katalon incorporates AI throughout the entire platform, ensuring swift testing and an optimized test automation workflow. Katalon enables users to revolutionize regression testing by autonomously creating tests based on real-time user behavior, pinpointing regressions crucial to users without the manual effort typically needed. Leveraging GPT as a co-author accelerates test script writing, allowing the application of GPT technology to generate Groovy code and custom keywords. Additionally, Katalon enhances script comprehension, facilitating seamless collaboration between experienced and junior developers. Lastly, the Virtual Data Analyst feature provides test analytics and actionable insights for comprehensive test reviews.

Functionize is an intelligent testing platform that combines AI, machine learning, and natural language processing (NLP) to create and run tests. Perfect for agile and DevOps teams that need fast, scalable, and reliable test automation.

Functionize offers AI-powered testing tailored for enterprises. The software delivers fully autonomous test generation utilizing deep learning models that create realistic tests using actual data from user journeys and past tests. Functionize's engine, rooted in big data and machine learning, constitutes a comprehensive AI-powered end-to-end testing solution hosted in the cloud. Noteworthy features include an architect with a next-gen ML test recorder, visual testing encompassing full-page or element-based testing, self-healing capabilities, and more.

Key features:

• NLP test generation. Testers can write test cases in plain English, and Functionize turns them into automated tests.
• Self-healing tests. The tool adapts tests as the app UI changes, reducing maintenance.
• Cross-platform. Functionize supports web, mobile, and desktop applications.

Code Intelligence adds security to testing by fuzzing and vulnerability detection. The testing tool helps developers find and fix security bugs before release.

Key features:

• AI Fuzzing. The AI testing tool generates random inputs to stress test the app and find security vulnerabilities.
• Real-time feedback. Code Intelligence tells developers about potential security issues to fix during development.
• Works with CI. It works seamlessly with continuous integration tools to enable continuous security testing.

Eggplant AI is an advanced testing tool that uses AI and machine learning to create optimized test paths. Its goals are performance, user experience, and functional testing, giving you a full view of the app quality.

Key features:

• Predictive testing. The testing tool uses AI to predict the most likely paths that will break the app.
• Automated test creation. AI generates test cases based on user behavior patterns.
• End-to-end testing. Eggplant AI offers cross-browser and cross-device testing and provides real-time reporting.

Current Trends in Testing Tools

The software testing industry is constantly evolving and improving. To choose the best tools, you need to be aware of the latest trends in the field and objectively assess their prospects. So, what's helpful to know about testing tool trends in 2024?

AI-powered test support

AI is becoming more advanced in automated testing and updating test cases when application code changes, which reduces the need for manual intervention.

Shift-left testing

Ai-powered test automation simplifies the testing integration early in the development cycle, known as shift-left testing. With this feature, teams can find bugs earlier and fix them faster.

AI in continuous integration/continuous delivery (CI/CD)

AI testing tools are deeply integrated into CI/CD, which allows for real-time feedback and immediate detection of issues.

Natural language processing (NLP) in test case creation

Some software testing platforms now use NLP to allow testers to write test cases in plain language, which AI then translates into executable code.

Predictive Analytics

Testing solutions powered by AI now use predictive analytics to forecast potential issues in future releases based on historical data, allowing teams to address bugs early.

Are There Any Downsides to AI Testing Tools?

Yes, like any new technology, there are pros and cons. You should also consider them when implementing AI software in your daily work.

Difficulty in setup

Setting up AI testing tools can be time-consuming and requires special knowledge to set up and configure correctly.

Costs

Of course, in the long run, automating the testing process is a profitable solution. But keep in mind that the initial investment in technology and training can be big.

Limited imagination

AI-powered testing is great at following patterns but struggles with creative problem-solving or testing for unexpected scenarios.

Need for good data

The accuracy of automated tests depends on the quality and quantity of data they have access to. So, problems with the source data can lead to wrong results even with the best AI testing tool.

Over-reliance

The more you trust generative AI, the more you start to ignore the value of manual testing, especially when it comes to user experience or exploratory testing.

AI Testing Challenges

While AI testing tools are amazing, they also come with various challenges that teams must overcome to use them effectively.

Data Quality and Availability

The outcome of AI is based on the data it uses. For AI testing tools to accurately predict behavior, identify bugs, or optimize test cases, they need large, high-quality data sets.

Some of the challenges teams face are:

• Not enough data. Not having enough historical data to train AI models will give you inaccurate or biased results.
• Irrelevant data. Feeding irrelevant or unstructured data to AI models can lead to distorted or unreliable results.

Data Privacy and Security

Security and privacy using AI become critical when dealing with sensitive data (finance, healthcare, etc.).

Algorithm Transparency

One of the biggest problems with AI is the “black box.” Many AI algorithms are not transparent to users, so testers and developers can’t understand how decisions are made.

It can complicate the explanation of test results, especially when working with medical, legal, finance, etc. applications.

Complex Test Scenarios

Not all tests can be automated by AI. Complex test cases that involve human emotions, user interaction, or dynamic environments are hard for AI to model.

For example:

• Usability testing. AI doesn’t have the empathy and subjectivity to evaluate the user experience.
• Edge cases. AI testing tools sometimes miss rare or unusual scenarios that can cause the software to fail in real life.

Legacy systems

Many companies are still running legacy systems for core business. Integrating AI testing tools with them can be a technical nightmare, as most innovative solutions are designed for modern cloud infrastructure.

Common Mistakes When Implementing AI Tools

Over automation

AI testing tools offer automation at an unprecedented level but can lead to overreliance on technology. Teams expect AI to handle everything and ignore the need for human oversight. AI can automate repetitive tasks and predict test cases. However, critical thinking, judgment, and creativity still come from human testers.

Tool misfit

With so many AI testing tools out there, it’s hard to choose the right one. A common mistake is using a tool that doesn’t fit the organization’s specific needs or isn’t scalable for future growth. Implementing the wrong tool will waste resources and be inefficient, so match the tool’s capabilities and the project’s requirements.

Not enough training and expertise

AI testing tools require some level of knowledge and expertise. Teams often underestimate the need for training and think AI will work out of the box. But without training, even the most advanced AI tools won’t be effective enough or will give wrong results and miss errors.

Ignoring the human factor

The human aspect of testing is often overlooked when introducing AI. New technologies are great at processing data and running scripts but can’t replicate human intuition, empathy, or creativity. When teams rely too heavily on AI, they risk missing essential elements of the user experience, such as design flaws or emotional reactions to interfaces.

Managing Expectations of AI Capabilities

One of the key things to get right when using AI testing tools is managing expectations. What to avoid?

AI is not perfect

Like any other tech, AI is flawed and has limitations. Tools can produce false positives or miss critical issues if trained on bad data without human testers supervising.

AI is not a replacement for testers

People get this one wrong. AI will not replace testers. AI should be seen as a tool to support testers, not a replacement for them. Testers still need to design tests, interpret results, and make decisions that AI can’t do, especially where they are inherently impossible.

Expectation and patience

Using AI testing tools is not a one-off. AI models need to be trained and refined continuously to work optimally. Teams should be prepared for an iterative process where AI tools evolve, getting better and better.

Conclusion

Integration of artificial intelligence technology is causing an important shift in the field of software testing. Although artificial intelligence testing tools have amazing benefits, they represent an evolution rather than an entire replacement of conventional testing techniques. Artificial intelligence testing tools have transformed the testing process by

automating time-consuming routine tasks ,designing robust test cases and

delivering quicker and more accurate error detection.Their main advantages are faster and more effective testing, lower manual intervention, improved test scope, reduced  long-term testing costs and intelligent root cause analysis  

However, artificial intelligence is a tool rather than a miraculous fix. Effective implementation calls for a careful choice of tools, ongoing learning and human supervision. 

The winning strategy for companies thinking about artificial intelligence testing technologies is strategic augmentation rather than wholesale replacement. AI should be seen as an intelligent assistant enhancing human knowledge rather than a complete substitution for qualified testers. AI testing tools will be essential to maintaining quality, speed, and reliability as the landscape of software development gets more complicated. The best approach will be a collaborative one where human creativity and computing capability of artificial intelligence cooperate.

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