Intelligent testing: 12 AI tools for test automation

Nadezhda Yushkevich
Updated on
Jun 21, 2024
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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.

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.


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.

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.

Top 12 testing tools with AI components description

Zebrunner testing platform serves as a unified hub for test management and automation reporting, leveraging AI/ML to identify root causes. This accelerates bug detection and resolution, streamlining the entire testing process. In the Zebrunner ecosystem, AI/ML functions as a collective intelligence trained by competent specialists from the customer’s team through a voting mechanism. This enables the model to independently assign categories to failures, expediting bug identification and clarifying responsibility for each issue.

As seen in the screenshot, 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.

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.

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.

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. 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.

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.

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. 

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 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.

About the author

Nadezhda Yushkevich

Content Writer and Tech Journalist

With 11 years of work experience in journalism, media management, PR, and content marketing, she has specialized in IT and startup areas for the last five years. Interested in practices, trends, and approaches in the field of quality assurance.