How many AI testing tools do you use to improve software quality? Or do you still think that the potential of AI is limited to creating unique art works and content? In fact, the capabilities that AI brings have already surpassed ChatGPT and have extended into software development and testing processes. If you want to know how to unify machine learning and test automation and benefit from leveraging AI for QA and testing, read more below.

Artificial intelligence in automation testing

Artificial intelligence is the ability of a machine to learn, modify and use data to solve any future scenarios. Artificial intelligence uses problem solving and reasoning to automate the testing process. Automation testing with artificial intelligence involves the use of AI and machine learning (ML) in existing automation testing tools to generate superior results and solve common problems in automation testing. Test automation using AI is known to accelerate the product lifecycle and increase an organization’s financial revenue. Artificial intelligence in automation testing helps in these areas:

• Authorization, execution and maintenance of automated tests.
• Product transparency.
• Early detection and correction of errors.
• Increasing overall efficiency.
• Accelerating the automation testing process.
• Generating relevant data for decision making.

IT testers maintain and modify numerous test cases on a daily basis. In this case, automated testing tools with artificial intelligence enable testers to seamlessly handle repetitive tasks, generate relevant data for decision-making, and detect and fix problems early in the development lifecycle. AI plays an important role in maintaining test automation suites and improving UI, unit test and also makes api testing more efficient. Overall, AI testing tools help testers in these cases:

• Detecting, correcting and eliminating errors and mistakes in the early stages.
• Detecting application changes and modifying the test case maintenance script.
• Improving the overall efficiency and transparency of testing processes.
• Accelerating quality test outputs.

Read our article Will artificial intelligence replace QA and IT testers in the future?

Artificial intelligence benefits in automation testing

Testing ensures customer satisfaction and protects possible application failures. The ability of AI testing tools to mimic human behaviour makes them very useful for testers involved in automated continuous testing processes. These tools identify changes more effectively than humans. Here are the various benefits of using AI in software testing:

• API test generation automation
• Performing visual testing
• Emerging and evolving robots
• Advanced error tracking
• Advanced test case writing
• Improved regression testing
• Greater test coverage
• Improved security
• Prognostic analysis
• Saving time and money
• Self-correcting tests

5 reasons to use AI to automate testing

#1. Increased accuracy

Automation testing together with AI and machine learning can easily identify visual defects and error trends and improve software quality.

#2. Faster product delivery

Automated maintenance is another reason to use artificial intelligence for testing. Since tests must be repeated and corrected in accordance with each new change in the source code, it makes sense to automate this process using AI and machine learning algorithms. This not only takes the burden off the testers, but also accelerate product launch time.

#3. Cost reduction

If you want to reasonably reduce costs, you can also apply AI to automate QA and testing. AI-powered tools free testers from repetitive tasks and allow them to focus on more complex problems. As a result, you can reduce the number of employees and the associated costs.

#4. Better test coverage

Unlike traditional test automation tools, their AI-powered counterparts are proving to be even more effective in terms of the depth and breadth of software analysis.

#5. Early feedback

The sooner errors are identified, the better. And AI-driven testing, as well as AI-driven QA automation, unlocks that possibility. By suggesting readable recommendations at the earliest possible stages, AI-based automation testing tools save effort for digital transformation teams.

#6. Scalability

More code requires more testing. As coding assistants such as GitHub Copilot allow developers to multiply their output, traditional testing methods are increasingly falling behind because they require too much manual effort to deal with so much code. However, artificial intelligence testing tools excel at performing tests on a huge scale. This scalability extends to testing a variety of devices, platforms and environments, providing broad coverage that enables reliable detection of bugs and vulnerabilities despite large amounts of code.

#7. Continuous improvement

Algorithm-based AI testing tools can be set up to learn and improve over time. By continuously learning about the software under test, these tools can start with a clean slate and iteratively improve test inputs based on the application’s runtime behaviour. This allows such tools to improve with each test run, leading to findings beyond the reach of most traditional testing tools.

Where to use AI-driven test automation?

• AI-based image recognition techniques can be used to create UI tests capable of assessing visual aspects and detecting interface errors.
• Using AI tools, API testing can be automated and unit tests can be streamlined.
• One of the other use cases for AI in test automation (example: Applitools) is related to regression testing.
• You can also use AI testing tools to speed up the self-repair process and reduce manual operations.
• And last but not least: the ability to automatically adapt test scripts to code changes makes AI-based tools a tangible part of the test maintenance process.

How to choose automation testing tools

With so many different automated testing tools available, it can be difficult to decide which tools will best suit your needs.

When choosing, testing and selecting automation testing tools, consider:

• What problem are you trying to solve – Start by identifying the feature gap in the automated testing tools you’re trying to fill to clarify the features and functionality the tool needs to provide.
• Who will need to use it – To assess costs and requirements, consider who will use the software and how many licences you will need. You’ll need to assess whether it’s just the QA software testers or the whole organization that will need access. Once that’s clear, it’s worth considering whether you prefer ease of use for all or speed for hardworking users of the tool.
• What other tools it needs to work with – Be clear about what tools you’re replacing, what tools remain, and what tools you’ll need to integrate them with, such as other testing tools, automation tools, or bug tracking software. You’ll need to decide whether the tools need to integrate together, or whether you can replace multiple tools with one consolidated automation testing tool.
• What results are important – Consider the result that the software must produce to be considered successful. Consider what skill you want to acquire or what you want to improve and how you will measure success. For example, the ability to gain greater insight into performance may be the result. You can compare the features of automation testing tools until you’re out of breath, but if you’re not thinking about the results you want to achieve, you could be wasting a lot of time.
• How it would work in your organization – Consider the software selection along with your workflows and delivery methodology. Evaluate what is working well and areas that are causing problems that need to be addressed. Remember that every business is different – don’t assume that because a tool is popular, it will work in your organization.

Artificial intelligence tools list for 2024

There is a long list of AI-powered test automation tools, and the team needs to choose the right one to tackle test automation tasks. Many AI-powered test automation tools perform intelligent bug finding and predictive self-repair, enable application resiliency, and automate business processes and workflows.

Here are a few AI automation tools that testers can try out in 2024. Read also our article CHATGPT and test automation.

1. Applitools

Applitools is a reliable automation tool with artificial intelligence for visual testing and user interface monitoring. Applitools is a comprehensive software testing platform that focuses on manual quality testing, DevOps and digital transformation. Powered by visual artificial intelligence, this tool allows the machine to mimic human behaviour. This AI-enabled application is enriched with an adaptive ML algorithm. The tool is robust with 3.8 times better stability, 45% higher defect detection and 5.8 times faster visual-enabled tests.

• It has a single line of code to parse the entire application screen.
• It uses AI and ML for test maintenance.
• It has a comparison algorithm that detects changes faster.
• It seamlessly integrates existing tests and eliminates the need to write and learn new tests.

2. Testim io

Testim.io is an automated functional testing tool that leverages artificial intelligence and ML to accelerate the authorization, execution and maintenance of test automation. It works well on a variety of browsers and platforms. It has a free basic plan with limited features and a pro plan that supports everything. This comprehensive AI testing tool reduces test creation and execution time by running multiple tests simultaneously, in parallel.

• It focuses on functional and user interface testing.
• It has intelligent locators to detect changes in the application while running automated tests.
• It integrates seamlessly with CI/CD tools and offers detailed error reporting.
• It overcomes unstable tests and slows down problems arising from frequent UI changes.
• It performs root cause analysis of failed tests.

3. AccelQ

ACCELQ is a cloud-based, codeless AI test automation tool that automates various aspects of the quality lifecycle: tests are performed from design through planning to execution. It’s a unique way to automate API and UI testing.

• It focuses on automating web UI, API, desktop and mobile platforms.
• It includes self-healing features, natural English programming, an intelligent element explorer and automated test generation.
• It offers a unified view of the entire QA lifecycle thanks to good integration with popular DevOps toolchains.
• It offers predictive analytics and path analysis for the development of test scenarios.

4. Functionize

Functionize is a cloud-based option for automated functionality, performance and load testing. It leverages artificial intelligence and ML to improve test creation, diagnosis and maintenance. It can perform a number of tests in minutes in all mobile browsers and desktops.

• This platform covers the declarative category of AI testing tools.
• It creates, verifies and maintains tests using AI and ML.
• Its SmartFix feature can easily test for failures and detect UI changes.

5. TestCraft

TestCraft is an artificial intelligence test automation platform used for regression and continuous testing. It also monitors web applications well. It uses AI to eliminate maintenance time and costs. This platform allows testers to visually create automated Selenium-based tests using a drag-and-drop interface.

• It uses ML algorithms to recognize the correct web elements when the application changes.
• It creates automated Selenium-based tests that testers can execute on multiple platforms and browsers.
• It allows you to create test models with On-the-Fly mode to reuse test steps.
• It comes with self-healing capabilities in the base package.

6. Katalon

Katalon is an AI-powered test automation tool that provides a complete solution for mobile app and website testing. It provides a built-in interface for testing. It has a robust object repository, support for multiple languages and other features:

• Creates reusable elements for improvised test automation.
• Can script in multiple languages including Java, C++ and others.
• Provides effective test results that ensure the quality of an application or site.
• Supports several types of testing, such as keyword-based testing, data-driven testing, etc.
• Has a user-friendly graphical interface.
• Integrates CI/DevOps workflows and other tools.

7. Selenium

Selenium is one of the most robust and versatile testing tools, making it a favorite among developers. The tool is now equipped with artificial intelligence, which provides extensive flexibility in running and executing tests. The open-source nature of this framework allows compatibility across platforms such as Linux, macOS and Windows. In addition, it works with a variety of browsers including Safari, Chrome and Firefox.

• Robust integration with various programming languages and frontend frameworks.
• Cross-platform and cross-browser compatibility.
• Efficient collaboration with source code compilation tools and continuous automation testing.
• Seamless management and generation of test cases and reports.

8. Code Intelligence

Code Intelligence combines dynamic testing and self-learning AI capabilities to identify bugs and vulnerabilities with every code change. Code Intelligence analyzes source code and allows developers to use information about the application’s runtime behaviour to generate intelligent test cases that uncover bugs and vulnerabilities that are often overlooked by conventional testing methods. Since this all happens automatically with every code change, developers get the exact line of code and input that caused the application to crash, so they can fix problems long before the product is finished.

• Possibility to access the entire source code.
• It identifies edge vulnerabilities and errors in the code at each change.
• It automates test case generation based on application behaviour to optimize test coverage.
• It performs automation testing of every code change to detect potential problems early.
• It allows reproduction, debugging and correction of all findings in the preferred integrated development environment.
• It facilitates compliance with various industry norms and standards.
• It prioritizes and schedules repairs using classification standards and defect monitoring.
• It supports Java, C/C++, Golang, and JavaScript, which can be tested.
• It enables rapid construction of AI-based tests with a functional unit test.

9. Eggplant Digital Automation Intelligence

Together with the power of artificial intelligence, Eggplant Digital delivers extensive coverage, improving user experience and accelerating product release cycles. With its easy-to-use interface, this tool offers a secure solution for testing any software on any platform or device, greatly improving the quality assurance process.

• AI-based testing to optimize user experience and accelerate release cycles.
• Compatibility with all types of software on different platforms and devices.
• It improves quality assurance processes with a fast, secure and easy-to-use interface.

10. AI Continuous Testing

Leveraging the power of artificial intelligence, Digital.ai Continuous Testing provides comprehensive coverage of functional, performance and accessibility cases. The cloud-based infrastructure supports scalable testing around the world while providing actionable insights through the Digital.ai Continuous Testing Lens. The tool excels in interoperability, fast test cycles, and features designed for non-programming team members.

• Scalable, cloud-based solution for manual and automation testing.
• It collects and analyzes AI-based metrics through the Digital.ai Continuous Testing Lens tool.
• It accelerates automation in test cycles, reducing time needed to product launch.
• User-friendly test editor for efficient test creation, validation and management.

Conclusion

The growing use of artificial intelligence tools has already changed the software testing environment. These tools bring many AI and ML benefits that traditional automation testing tools cannot offer. Combining traditional testing with AI tools can make software development more efficient by speeding up the development process and more accurately identifying even deeply hidden problems, allowing testers to deliver an even higher quality product.

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