Nowadays the use of Artificial Intelligence in the development process has a positive impact on team productivity. Thanks to this, the development team can speed up the creation of digital solutions. Consequently, the obvious need for software products in Quality Assurance testing — is also growing. Only modern tools based on the same innovative technology can satisfy this requirement.

According to Fortune Business Insights, it is an argument that the AI-powered testing market is rapidly growing. While it was valued at $857 million in 2024, by 2032, it is projected to increase to $3,824 million. Impressive, isn’t it 😃

⏯️ Learn more about AI-powered testing in a video from an industry expert: Expert Talk on AI in Testing – Jason Arbon

Due to this trend, in this article, testomat.io team decided to highlight the immense potential of AI testing tools. We will also introduce you to various platforms that can optimize your testing efforts.

AI Testing Tools Definition

AI-based testing tools are powerful solutions for enhancing the speed of your testing duties. They allow you to automate and optimize various aspects of stages of the Software Development Life Cycle. In this way, such platforms help QA teams ensure high software quality and demonstrate impressive speed carrying on through workflow.

AI-based tools combine the capabilities of machine learning, natural language processing, and other technologies based on artificial intelligence (for instance its hearts are GPT -4, ADA LLama 2, Ollama models etc.).

The top 10 most popular AI models, as revealed by the Orca 2024 State of AI Security Report

Especially for QA Managers AI enables to improve the accuracy of testing results, enhance test coverage, and gain other advantages.

Benefits of Using an AI-Based Testing Platform

Modern development teams are required to maintain exceptional standards of software quality. This can be challenging. AI testing tools are designed to make this task easier with a range of advantages.

✅ Good scalability of AI engine

Surely, in the practice of almost all Agile teams, there have been instances of project expansion. Often, in such situations, the platforms used fail to handle the assigned tasks.

Choose an AI testing tool and this problem won’t concern you. These platforms handle generating, improving and identifying duplicates within numerous test cases very well. How is that possible? You reduce the need for manual testing and, as a result, speed up the QA process.

✅ Improved test coverage

Artificial intelligence-based platforms can create complex test cases. They can cover a variety of scenarios you even can not imagine. Including boundary cases or invalid input data. Furthermore, such tools use self-learning algorithms. This means that you will be able to systematically increase test coverage.

✅ Wide integration capabilities of AI tools

You can integrate AI-based tools with almost any of the platforms used in the project. For example, they work excellently in conjunction with the Jira bug tracking system, GitHub or the Jenkins continuous integration service.

✅ Continuous learning and improvement

At the core of AI-based testing platforms are different self-learning algorithms. They are designed to improve over time through systematic self-learning. During testing, they analyze your App’s progress. Then artificial intelligence algorithms take it into account for subsequent improvement of test input data. Thus, the accuracy and effectiveness of AI test runs often surpass test results on conventional platforms.

Here’s what the CEO of testomat.io, Mykhailo Poliarush, says about the advantages of using AI-based testing tools:

“Use AI-driven tools to automatically generate and update test cases based on application changes. This approach reduces the manual effort involved in maintaining test suites and conducting regression testing — ensures that your tests stay relevant as the application evolves.”

Now it makes sense to list the key features of AI tools. They make them indispensable in modern projects.

What Are the Key AI Capabilities for Software Testing?

AI-based tools use machine learning, data analytics, and other advanced technologies. This enables artificial intelligence to improve several aspects of software testing simultaneously. Its key capabilities include:

#1: Test Case Generation Using Machine Learning

The principle of test generation on AI platforms is as follows. The system analyzes the code of the digital solution and user interactions. Then, it examines existing test cases and historical data if they exist. Based on this, it automatically generates new test scenarios relevant to the current version of the tested application.

Capabilities of AI tools in intelligent test case generation:

Boundary case coverage. This capability enables AI-driven testing tools to identify issues that a human tester might inadvertently miss.
Swift adaptation to codebase changes. As new features are introduced or bugs are resolved, the AI dynamically adjusts test cases to align not only with the latest version of the software. This ensures the tests remain accurate and relevant.
Enhancing test coverage. The goal of any AI testing tool is to cover the most important parts of the application. So, we can prompt it as well. This ensures accurate testing compromising with effectiveness.

#2: Test Execution Optimization with Predictive Analytics

Test execution optimization is another primary task of AI-based testing tools. To achieve this, such platforms use predictive analytics. This is achieved through the use of predictive analytics, which analyzes historical data to identify patterns and assess the likelihood of specific tests failing.

Benefits of predictive analytics:

Faster testing cycles. The AI prioritizes tests with a higher probability of failure, reducing the need to execute every test case.
Better resource utilization. QA teams can dedicate their efforts to ensuring the functionality of the software’s most essential components.
Dynamic testing strategies. The system adapts in real time, deciding which tests to execute based on current needs.

#3: Defect Prediction and Root Cause Analysis

AI testing tools can estimate the likelihood of defects occurring in particular modules of the application. For this purpose, the system analyzes historical data, code changes, and results from previous test runs.

Here is what an AI-based testing platform can do:

Defect prediction. Artificial intelligence suggests to testers where problems are most likely to occur. This allows the team to focus on testing functions with the highest risk levels.
Root cause analysis. The platform swiftly traces defects to their source, whether it’s faulty code, misconfigurations, or inaccurate test cases. This significantly speeds up the troubleshooting process while minimizing system downtime.
Comprehensive defect analysis. Through detailed analysis, QA teams can make informed decisions to improve test coverage, efficiently resolve issues, and implement measures to prevent their recurrence.

#4: Continuous Testing in CI\CD Pipelines with AI

AI facilitates the seamless integration of the testing tool with CI\CD pipelines. This ensures continuous testing in projects.

Key features of AI testing tools in this area include:

Test automation. With each minor update to the codebase, AI tools turn test execution automatically, helping QA engineers catch issues early in the software development lifecycle (SDLC).
Real-time adaptation. AI tools adjust testing activities dynamically in response to code changes, ensuring relevance and efficiency.
Accelerated feedback loops. With AI, testers can recognize the critical functionalities and problematic areas, gaining rapid insights and making informed decisions about which tests to execute.

#5: Self-Healing Test Scripts

AI-powered platforms are capable of creating self-healing test scripts, which automatically adapt to changes in the software code. This guarantees that automated tests stay dependable and current.

What this feature in AI testing tools provides teams:

Automatic script updates. When a test fails due to code changes, the AI modifies the test script autonomously, aligning it with the new conditions.
Reduced resource consumption. Teams save time and effort on manually updating test scenarios, ensuring reliability even in fast-paced development environments.
Improved accuracy. By increasing test coverage and ensuring test execution, AI testing tools deliver highly precise results.

These are the core features inherent in many leading platforms. Others may vary from tool to tool. To help you select the optimal solution, we present a list of the best AI testing tools with advanced capabilities.

Top AI Software Testing Tools to Streamline Test Process

The modern market for intelligent test automation solutions offers a wide variety. It includes platforms capable of testing various types, such as performance testing, API testing, regression testing and more. Some are designed for assessing the quality of web applications, while others are suited for detecting defects in mobile apps.

We believe that such tools represent the future of software testing. This viewpoint is shared by ASTQB experts:

Software testing is experiencing a transformative change with Artificial Intelligence (AI) at the forefront. Gone are the days when AI was merely a concept of the future. Today, it is a dynamic force reshaping error detection and correction, heralding a new chapter in software development.

Below, you will find a list of tools that are sure to meet the needs of your team.

Testomatio

The testomat.io is a powerful test management system that leverages artificial intelligence capabilities for test automation and efficient test case management.

AI enables users of the platform to automate test documentation, automatically generate test code, and analyze their projects.

List AI-based generative features to simplify & improve your workflows:

Automatic Living Documentation generation. The system analyzes test cases and generates documentation in real time based on them. This eliminates the need for manual documentation, its maintenance, reducing time costs.
Test description creation. Testomat.io can also analyze test code and generate human-readable descriptions of tests. This allows non-technical team members to participate in testing.
Automation code generation. This feature is the reverse of the previous one. Provide the system with a test description, and it will recommend the pieces of code to automate that test.
Responding to project queries. With this TMS, you can obtain any information about the testing progress in seconds. Extract data, identify and resolve issues — all without the need to manually sift through large amounts of data.
Failure detection and root cause analysis. The system examines test logs and explains the causes of failures based on them. This significantly reduces debugging time.
Test set compliance control with related Jira issues. This test management system features bidirectional integration with Jira. Users can link tests to Jira issues and run them directly from the project management system. AI ensures that testing efforts comply with the requirements documented in Jira and other bug-tracking tools, for instance, more modern Linear. This is crucial for tracking testing effectiveness.

Katalon Studio

Katalon Studio is an AI-based testing tool positioned by its creators as an “all-in-one test automation solution.” And it truly is — this platform is suitable for quality testing of web, desktop, and mobile applications.

Artificial intelligence optimizes testing efforts with Katalon. Its capabilities are aimed at test automation, intelligent test execution, and test result analysis.

Here are the main AI Katalon features:

Object identification. In the process of test automation, the platform recognizes objects using AI algorithms. This improves the reliability of tests by minimizing their dependency on static locators. In other words, when the elements of the user interface change, the tests will be less prone to failures.
Self-Healing Tests. The tool automatically detects changes in various system components and adapts the object locators accordingly. This reduces the need for manual efforts to maintain the relevance of test scripts.
Test execution optimization. In this area, Katalon Studio’s capabilities are limitless. The platform can eliminate duplicate and redundant tests. AI also plays a key role in creating effective testing strategies. For example, test case prioritization is available, meaning the team can first test the most critical functionality. If needed, tests can be parallelized.
Automatic test case generation. The tool takes the application’s functionality as the basis and generates test cases based on it. This speeds up the creation of large test sets, as it doesn’t require manual intervention.
Visual testing. Within this QA process, AI detects discrepancies or regressions in the user interface. It compares screenshots with current UI visual elements. This way, you can identify defects that may not be found during standard functional testing.

Applitools

Applitools is a comprehensive solution that combines leading technologies for automating test creation and test maintenance in modern Agile projects. Among them are visual AI, GenAI, and no-code methods. This streamlines the testing process, minimizes the reliance on manual testing, and broadens test coverage.

Here is how the Applitools tool’s AI features make life easier:

Smart Image Comparison with Visual AI. The platform leverages machine learning to compare baseline screen images with test snapshots, spotting even the tiniest changes.
Automatic element identification. It recognizes user interface elements on its own, saving time and reducing effort. This approach helps increase the efficiency of visual testing by reducing the number of false positives and false negatives.
Handling dynamic elements. Applitools can handle dynamic content, including animated elements. Unlike static elements, they change over time. Therefore, the tool only detects significant changes
Accessibility Testing. Visual AI helps in testing the visual accessibility of applications, which is important for users with disabilities.
Cross-browser and cross-device testing. Thanks to AI, testers do not need to manually configure each test variant. Applitools does this automatically.

Testim

Testim.io is a test automation tool that leverages artificial intelligence to deliver quality software products. With this ALM platform, you can test Salesforce, web, and mobile apps. This versatility makes the solution a natural choice for many teams.

What AI features does Testim offer to users? Here are the key ones:

Self-Healing tests. Even if a user interface element changes in some way, the tests will continue to run successfully without manual adjustments. This simplifies test maintenance, which is especially important in large-scale projects.
Identifying elements using smart locators. Using machine learning, Testim.io ensures the proper functioning of tests even when the UI structure changes.
Identification of unstable tests. AI empowers the platform to pinpoint genuinely problematic tests while ignoring temporary glitches, helping teams focus on what really matters.
Automatic test case creation. It observes user interactions, documents actions, and transforms them into test scenarios — saving you the hassle of writing tests manually.
Plain English to test code. Simply describe the function you want to test, and the platform will generate the necessary code for you.
Optimization of existing tests. AI helps quickly identify issues in test cases, improving debugging speed
AI-powered visual testing. The tool detects even the slightest changes that may negatively impact the user experience.

Mabl

The Mabl platform uses generative AI and other artificial intelligence based technologies to enhance test automation. They optimize all aspects of testing, including test case generation, test maintenance, and test execution.

AI-powered features of Mabl that users can take advantage:

GenAI test generation. QA teams can simply use natural language to automatically create test cases, no matter how complex the testing scenarios are.
Early detection of unstable tests. The AI swiftly spots potential failures, alerting the team before problems escalate. This helps increase the stability of testing.
Auto-healing. The platform automatically adapts tests to changes made in the code. This approach significantly simplifies test maintenance.
Visual testing. The tool clearly identifies unexpected UI changes by comparing the baseline with the current version.
Availability analysis. Mabl identifies accessibility issues on digital solution pages and prioritizes them. This allows development teams to understand which issues should be addressed first.
Intelligent waiting. The tool tracks product performance and includes this information in the testing process timeline. This improves the reliability of tests.

Copado

Copado is a platform focused on DevOps and CI\CD for the development, testing, and deployment of Salesforce solutions. AI is utilized throughout each stage of the digital product lifecycle. However, since we’re focusing on testing, let’s dive into how it enhances that area specifically.

Copado benefits from the following its AI-driven features:

Prioritization of test cases. The platform automatically identifies and selects tests that are most relevant, helping teams save time and resources.
Quick creation and modification of test scenarios. The tool provides easy prompts for quickly creating or adjusting complex test scenarios.
Predictive analytics. AI analyzes test results and predicts potential defects, allowing teams to spot issues early and address them proactively.
Code quality assessment. The platform assesses the quality of test code and offers suggestions for improvement.
Test conversion. The tool helps convert tests to different formats, making it easier to adapt to various testing environments.

AI Test Generator

AI Test Generator is a tool that can significantly ease the life of QA teams. Its task is intelligent test case generation for different types of testing. Users note its impressive ease of use and versatility. With this solution, you can automate various types of testing, including functional testing, regression testing, performance testing.

Here are its key AI Test Generator features:

Automatic test generation. The platform creates test cases based on input data or user requirements. AI also analyzes system behavior to identify which scenarios need testing.
Optimization of test runs. The AI Test Generator looks at historical data, identifies patterns, and recommends the most relevant and effective test cases for priority execution.
Automatic identification of test cases. The platform ensures no critical test cases are missed due to human error, improving overall test coverage.
Automation of test case creation within CI\CD pipelines. The tool integrates seamlessly into a project’s CI\CD pipeline, ensuring the correct tests are generated and executed automatically during workflows.
Test case customization. While the tool automates test generation, testers can still define parameters like application type, testing goals, and more.
Test documentation creation. The platform automatically generates documentation for the testing process, making it easy for the team to review test descriptions, expected outcomes, and steps.

AccelQ

AccelQ is a cloud-based test automation tool that utilizes artificial intelligence in its processes. The platform is designed to ensure continuous testing on your project, focusing on high-quality automation. It is suitable for web and mobile testing.

Key AI features of AccelQ include:

Self-healing tests. The platform tracks even the slightest changes in the user interface and automatically adjusts the test according to the current code base. This process requires no manual intervention and reduces the effort needed for test maintenance.
Dynamic object identification. AccelQ can identify UI elements in real-time during software testing, supporting a variety of devices and browsers.
Automatic test case generation. The solution analyzes the system’s behavior and, based on its functions and processes, creates relevant tests. This accelerates the testing process and improves test coverage.
Test run prioritization. AccelQ tracks code changes and identifies which tests should be prioritized based on those updates.
Predictive analytics and AI reporting. The tool examines historical data to highlight high-risk areas and generates detailed reports on testing progress and outcomes.

Each of these tools offers unique features and varying costs. When selecting an AI testing tool, it’s important to strike the right balance between price and quality. Many platforms also offer free trials to help you make the best choice.

Potential Issues of AI-Based Testing

AI testing tools have a vast number of advantages. However, is everything as flawless as it seems? If you’ve decided to integrate artificial intelligence into your QA processes, you should be aware of the potential risks that you may encounter.

Lack of Human Control. AI-based testing is effective, fast, and simple. However, you should not blindly rely on artificial intelligence. For example, when it comes to user experience, AI may overlook some important details.
Limitations in Handling Edge Cases. Most tools excel at testing standard test scenarios. However, not all test automation tools are capable of handling edge cases effectively.
Dependence on the Quality of Machine Learning Model Training. The effectiveness of AI-based testing depends on the quality of the data on which the machine learning model has been trained. If the data quality is low, the results may be inaccurate.
Opaque Nature of Blackbox AI. Many test automation platforms use machine learning models that function like a “black box,” meaning their internal processes cannot be interpreted by humans. As a result, it may be difficult to trust the obtained results.
Data Privacy and Security. AI-based testing involves processing large datasets, often containing confidential information. This raises concerns regarding its security.

Bottom Line

AI testing tools are changing the way testing process is done in today’s Agile projects, making it simpler and faster. These tools can handle complex test scenarios, work with large datasets, and adapt to different types of testing across a wide range of devices. Teams using AI-driven testing have reported faster QA workflows, fewer errors, and improved product quality.

When selecting the right tool, be sure to evaluate its functionality and cost. The good news is, you will start seeing the results of your investments quickly.