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Alexander Thiam14 min read

Top 18 AI Testing Tools in 2024

The impact of AI tools on software development is starting to make itself felt. As the productivity of developers increases, so does the necessity for software testing. Luckily, AI is also seeing increasing adoption in testing, enabling dev teams to ensure robust and secure software despite increasing output. In this in-depth guide, we will explore the top 18 AI testing tools in 2024 that have the potential to take your testing processes to the next level.

Overview:

Why We Need AI Testing Tools

Fast-paced release cycles, increasing third-party dependencies and growing API reliance have presented software testing with some serious challenges these past years. As a result, 45% of software is being released without appropriate security checks. This trend is manifesting itself in roughly 50% of organizations reporting at least one security incident over a period of 12 months (recorded in 2022).  Now, as AI coding assistants have entered the chat, these numbers are likely to worsen, as research findings suggest that such coding tools produce equally insecure or more insecure results than humans. Even if AI coding assistants become more secure over time, the sheer increase in output will induce calls for more scalable testing solutions. Luckily, AI-powered software testing tools can help to ensure that testing keeps pace with these developments.

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What Are the Benefits of AI Testing Tools?

To hold external threats at bay and meet modern quality and security standards, AI testing tools are becoming indispensable. Let's explore some of the advantages that are driving this shift:

Scalability

More code requires more testing. As coding assistants such as GitHub Copilot are enabling developers to multiply their output, traditional testing methods are increasingly falling behind as they require too much manual effort to address such quantities of code. AI testing tools, however, excel at performing tests on a massive scale. This scalability extends to testing various devices, platforms, and environments, ensuring broad coverage that enables reliable bug and vulnerability detection despite large amounts of code.

Continuous Improvement

AI testing tools running on genetic algorithms can be set up to learn and enhance over time. By learning about the software under test on the go, these tools can start with a blank slate and iteratively improve test inputs based on the application's behavior during runtime. This allows such tools to get better with every test run, leading to findings beyond the reach of most traditional testing tools.

Surpassing Manual Testing Limitations

While a traditional unit test tests one case at a time, AI tools can enhance it to test the same function or API with hundreds of thousands of unexpected or invalid inputs. This allows developers to automatically find bugs and vulnerabilities in places they never even would have thought to look.

Increased Test Coverage

Self-learning algorithms can be leveraged to continuously increase test coverage with each new input. While traditional dynamic testing methods (and hackers) treat the application under test as a black box, this white-box approach gives dev teams full visibility into the internal workings of the software. Increasing code coverage allows dev teams to gain a leg up over attackers, as they can use the source code to their advantage. Additionally, having access to the source code simplifies debugging, as findings can easily be traced back to the erroneous section of the source code.

18 Best AI Testing Tools in 2024

Given the enormous improvements in machine learning and self-learning AI, AI-powered software testing tools have gotten significantly better over the past years. Below, we will delve into some of the top AI software testing tools available today.

1. Selenium

Selenium is one of the most robust and versatile testing tools, making it a favorite among developers.  The tool is now equipped with AI to provide extensive flexibility for running and executing tests. The open-source nature of this framework enables compatibility across various platforms, such as Linux, macOS, and Windows. Moreover, it works with various browsers, including Safari, Chrome, and Firefox.
  • Robust integration with diverse programming languages and frontend frameworks
  • Compatible with various platforms and browsers
  • Effective collaboration with tools for source code compilation and continuous automated testing
  • Seamless management and generation of test cases and reports

selenium

Code Intelligence

Code Intelligence combines dynamic testing and self-learning AI capabilities to identify flaws and vulnerabilities with each code change allowing more robust automated testing to occur throughout the development lifecycle, assuring high-quality code while seamlessly integrating with various coding environments. The power of Code Intelligence lies in its ability to access the source code, which allows developers to leverage information about an application's runtime behavior to generate intelligent test cases that can expose bugs and vulnerabilities that conventional testing methods frequently overlook. As all of this occurs automatically with each code change, developers are provided the precise line of code and input causing the crash so they can resolve issues well before a product is complete.

Key Features

  • Ability to access entire source code.
  • Identifies edge-case vulnerabilities and flaws in your code with each change.
  • Automates the generation of test cases based on the application's behavior, thereby optimizing test coverage.
  • Performs automatic testing for each modification to the code to detect potential problems early
  • Enables reproduction, debugging, and correction of all findings in your preferred integrated development environment.
  • Facilitates conformity with various industry norms and standards.
  • Prioritizes and schedules fixes using classification standards and bug monitoring.
  • Supports Java, C/C++, Golang, and JavaScript can be tested.
  • Permits rapid construction of AI-powered tests with a functional unit test.

code intelligence

 

3. Functionize

Functionize allows developers to execute end-to-end tests that are “self-healing” and capable of running at scale in the cloud. It promotes faster development, decreases expenses, and improves quality in continuous integration/continuous deployment (CI/CD) environments. With Functionize, your teams can test faster, spend less time on trivial tasks, and reduce overall QA stress, all while fostering a forward-thinking testing environment.

Key Features

  • End-to-end tests that are self-healing and cloud-scalable
  • Faster testing times and lower costs
  • Low-code intelligent tests that future-proof your team's skills
  • Integration with infinite DevOps cycle for increased automation

 functionize

 

4. Testsigma

Testsigma uses AI to make test automation up to 5 times faster, allowing you to ship products with greater confidence. Empowering functional QAs to automate tests in plain English, Testsigma offers a platform to test web, mobile, and desktop apps, as well as APIs. It effortlessly integrates with your CI/CD pipeline for continuous testing, enabling seamless and efficient test management and TestOps experience.

Key Features

  • Quick and efficient test automation powered by AI
  • Plain English test automation, enhancing accessibility for functional QAs
  • A singular platform for diverse testing needs, including web, mobile, desktop apps, and APIs
  • Smooth integration with CI/CD pipelines for continuous testing

 testsigma

 

5. Katalon Studio

Katalon Studio prioritizes quality at the UI layer, using AI to eliminate false positives and deliver a seamless customer experience. It also offers visual testing capabilities that provide clear-cut ROI by saving significant human effort and time. Adding AI Visual Testing to existing Katalon Studio tests is a breeze, requiring no additional tests, and can be run parallel with functional tests for comprehensive coverage.

Key Features

  • AI-powered visual testing for UI and UX, reducing false positives
  • Seamless integration of AI Visual Testing into existing tests
  • Capability to run visual and functional tests in parallel
  • Smart comparisons to identify significant changes to the application's design and content

katalon-studio

 

6. Applitools

Applitools represents the next generation of test automation platforms powered by Visual AI. This platform significantly reduces the time required for creating, executing, and maintaining automated tests, replacing traditional functional testing with AI. By reducing manual work across the entire testing process, Applitools allows teams to test their applications in-depth, boosting overall quality while accelerating delivery and lowering costs.

Key Features

  • Reduction in time for creation, execution, and maintenance of automated tests through Visual AI
  • Large-scale application testing capability
  • Intelligent test automation that minimizes manual work across the testing process

 applitools

7. Eggplant Digital Automation Intelligence

Eggplant Digital employs a model-based digital twin testing strategy; along with the power of AI, Eggplant provides vast coverage, enhances user experience, and prompts release cycles. With its easy-to-use interface, this tool offers a secure solution to test any software on any platform or device, significantly improving your quality assurance process.

Key Features

  • Model-based digital twin testing approach for comprehensive coverage
  • AI-powered testing to optimize the user experience and speed up release cycles
  • Compatibility with all types of software across various platforms and devices
  • Enhances quality assurance processes with its fast, secure, and easy-to-use interface

eggplant-keysight

8. Digital.ai Continuous Testing

Harnessing the power of AI, the Digital.ai Continuous Testing tool provides comprehensive coverage for functional, performance, and accessibility use cases. With a cloud-based infrastructure, it supports scalable testing across the globe while delivering actionable insights via the Digital.ai Continuous Testing Lens. The tool stands out with its interoperability, rapid testing cycles, and features designed for non-coding team members.

Key Features

  • Scalable, cloud-based manual and automated testing solution
  • AI-powered metrics collection and analysis through the Digital.ai Continuous Testing Lens 
  • Accelerates automation in testing cycles, reducing time-to-market
  • User-friendly Test Editor for efficient test creation, validation, and management

digital.ai

 

9. TestCraft

TestCraft by Perforce offers a robust Selenium-based automated testing solution. This tool provides a codeless testing approach, enabling non-technical users to create and execute tests. With its AI/ML technology, it reduces the burden of maintenance while supporting remote work and collaboration. TestCraft balances speed, scale, and quality in web app delivery.

Key Features

  • Codeless and automated testing, empowering non-technical users
  • AI/ML technology for reduced test maintenance
  • Cloud-based solution promoting remote collaboration
  • Supports extensive testing activities across different browsers and devices

testcraft

10. Testim

Testim aims to increase test coverage and reduce maintenance through its innovative AI-powered platform. It enables swift authoring, providing quality across applications, while its self-improving stabilizers eliminate flaky tests. Testim is quick in troubleshooting by providing comprehensive insights and control over testing operations.

Key Features

  • Fast authoring for increased test coverage and quality
  • AI-powered stabilizers to eliminate unstable tests
  • Efficient scaling of testing operations with enhanced control and management
  • Quick troubleshooting with tools to pinpoint root causes

testim-tricentis

 

11. mabl

mabl is a powerful, low-code ai test automation solution offering reliable end-to-end test coverage with significantly less effort. Its platform seamlessly integrates load testing, enabling teams to shift performance testing earlier in the development lifecycle. mabl reduces infrastructure and operations costs while speeding up the production of the applications.

Key Features

  • Low-code, integrated test automation solution offering load testing
  • Shifting of performance testing earlier in the development lifecycle
  • Enables high-velocity software teams to create, execute, and maintain reliable tests
  • Reduces infrastructure and operations costs

 mabl

 

12. Watir

Watir is an open-source web application testing tool based on Ruby. It simulates real user interactions with web applications, supporting cross-browser automation testing. Lightweight and user-friendly, Watir provides simple, readable, and maintainable tests, making it a flexible solution for any web application.

Key Features

  • Open-source web application testing tool
  • Simulates user interactions across different browsers
  • Creates simple, readable, and maintainable tests
  • Flexible solution for any web application regardless of its programming language

 watir

13. Sauce Labs

Sauce Labs’s solution to the testing landscape is introducing low-code automated web testing services and empowering citizen testers to match the productivity of top-notch automation engineers. The tool's AI-powered systems enable authoring, writing, and full automation of test suites, even for those with little to no programming knowledge. This not only democratizes testing but also fosters a collaborative environment with automation engineers who can focus on more code-specific aspects of software development.

Key Features

  • Low-code automated web testing, empowering citizen testers
  • AI-powered systems for authoring, writing, and full automation of test suites
  • Promotes a collaborative work environment
  • Democratizes web application testing

saucelabs

14. Tricentis

Tricentis offers an extensive suite of test automation capabilities designed to tackle the most pressing challenges of software testing. The tool leverages AI and cloud technologies to reduce test cycle times and errors, focusing on what matters most. It increases visibility into testing activities, enhancing release confidence and collaboration.

Key Features

  • A comprehensive set of test automation capabilities
  • AI and cloud-powered tools to fast-track software delivery and reduce costs
  • Risk-based, codeless approach to testing
  • Boosts release confidence with improved visibility into testing activities

 tricentis

 

15. SmartBear VisualTest

SmartBear VisualTest brings AI-powered visual test automation to your toolkit. It integrates with existing UI tests, allowing you to verify the look and feel of your app quickly. This tool uses next-generation machine learning to track different types of visual changes, ignoring false positives and reducing time spent on redundancies.

Key Features

  • AI-powered visual test automation Integrates with existing
  • UI tests for quick verification of the app's look and feel
  • Uses next-gen machine learning to track visual changes, ignoring false positives
  • Reduces time spent on redundancies

smartbear

 

16. ACCELQ

ACCELQ is a cloud-based, AI-powered codeless test automation platform. Its unified platform caters to a range of channels, from Web, Mobile, API, Desktop, to Backend. ACCELQ provides business process-focused automation integrated across the tech stack, enabling powerful handling of real-world complexities without requiring code. The platform stands out with its quality technology for Cloud & Packaged App Testing & Automation.

Key Features

  • AI-powered codeless test automation on the cloud
  • Unified platform for various channels, including Mobile, Web, API, Desktop, and Backend
  • Automation with a focus on business processes and integrated throughout the tech stack 
  • Transformational technology for Cloud & Packaged App Testing & Automation

accelq


17. Parasoft

Parasoft provides a continuous quality platform allowing easy control of your test environment. It offers automated end-to-end testing to deliver quality software at scale within minutes. From code to UI, Parasoft's solutions span every phase of the development process. The platform increases code and test coverage, validates application reliability, and verifies test results, ensuring quality deliverables.

Key Features

  • Automated end-to-end testing for software delivery at scale
  • Solutions spanning every step of the development process
  • Increases code and test coverage for application reliability
  • Verifies test results and identifies gaps via reporting and analytics dashboard

parasoft

18. TestRigor

TestRigor takes a user-centric approach to testing. It allows the team to focus on what needs to be tested instead of being burdened by coding based on implementation details and test maintenance. Its test automation tool creates, edits, and updates tests of any complexity, expanding test coverage rapidly. Unlike most codeless test automation tools, TestRigor identifies elements as seen on the screen, providing stable tests for both Desktop and Mobile browsers and Native mobile applications.

Key Features

  • User-centric end-to-end test automation tool
  • Allows for creating, editing, and updating tests of any complexity
  • Identifies elements as seen on the screen
  • Provides stable tests for Desktop and Mobile browsers, and Native mobile applications

testrigor

Conclusion

Combining traditional testing with AI tools can make software development more efficient by accelerating the development process and identifying even deeply hidden issues more accurately. If you are interested in how LLMs and self-learning AI can be combined to overcome some of the biggest barriers in software testing, we recommend you check out our whitepaper on the future of productive development.

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How do AI testing tools work?

AI testing tools leverage artificial intelligence and machine learning algorithms to automate the process of software testing. They can learn from past test scenarios, predict and identify bugs, and enhance the overall scalability of testing strategies.

How is AI used in software testing?

AI can be used in software testing in several ways, including generating test cases, predicting potential bugs, analyzing test results, and optimizing the testing process. 

What are the pros and cons of using AI testing tools?

AI testing tools can enhance test coverage, accelerate testing cycles, and improve the quality of software. However, they can require a big investment and a steep learning curve to be utilized efficiently. Also, they may not completely eliminate the need for human input, especially for complex, non-repetitive tasks.

How does Code Intelligence differ from other AI testing tools?

Code Intelligence uses a white-box approach based on self-learning AI that allows continuous testing with every code change. It stands out for its ability to identify deeply hidden bugs and vulnerabilities early in the development process.

What are the best AI testing tools on the market?

Several excellent AI testing tools are available in the market, such as Code Intelligence for dynamic white-box testing or  watir for dynamic black-box testing or  Parasoft for static analysis.