AI Testing Tools: The Must-Have Features

AI has revolutionized QA testing as we know it. QA testing tools significantly impact application performance, reliability, and quality. While the market for traditional software quality testing tools is mature and has a few well-known frameworks teams can use for many test use cases, AI testing tools have burst onto the scene to supercharge a team's testing efforts. Of course, this new suite of tools makes for a new set of decision points for testing teams to consider. 

To help software teams find the right AI testing tools for their needs, this article explores the transformative power of these tools, how they can improve existing test frameworks, and nine essential AI testing features you should know. By the end of this article, you'll be armed with the knowledge to make an informed decision and confidently choose a QA testing tool that suits your use case and technical requirements.

AI testing tools vs. traditional frameworks

AI testing tools provide several unique advantages over non-AI-powered automation frameworks for QA testing. AI test tools boast impressive features, such as automated test generation and maintenance, anomaly detection, and enhanced analytics and reporting. 

Creating and maintaining your platform that leverages the benefits offered by AI is a monumental undertaking. Unless you have significant AI capabilities in-house, consider an off-the-shelf solution that requires minimal setup and onboarding while delivering an impact on your test processes quickly. However, let’s start by looking at some of the most popular open-source testing frameworks that AI testing platforms leverage to deliver test automation.

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Top open-source frameworks leveraged by AI testing tools

These open-source testing frameworks are some of the most popular tools used by QA engineers and leveraged by leading AI platforms to build automation around:

• Selenium: A versatile testing framework for web applications that supports multiple languages and browsers.
• Cypress: An all-in-one JavaScript testing framework designed for modern web applications with real-time reloading and integrated debugging.
• Playwright: A Node.js library from Microsoft for browser automation that supports multiple browsers and provides tools for web testing across devices.
• Puppeteer: A Node.js library developed by Google for controlling headless Chrome or Chromium, primarily used for automating and testing web applications.

The most critical factor in your choice of QA testing tool is the feature set it boasts, regardless of whether that tool is open-source or a paid AI-enabled offering. Let’s explore some of the key features that QA tools should offer.

Nine essential features of AI testing tools

The table below summarizes the nine features of AI testing tools we will explore in detail in the sections that follow.

Automated test framework creation

Creating an automated test framework boosts efficiency by reducing manual work and ensuring consistent, accurate tests. It increases test coverage and accelerates development cycles, resulting in higher-quality software. AI testing tools make this even more straightforward. Qualiti is one AI solution that allows you to generate test cases in English, as if you were writing a manual test, or can generate them for you after exploring your application and documentation or by optionally including a snippet in your frontend code:

<script
  async
  defer
  type="text/javascript"
 src="https://streaming.qualiti.ai/qualiti-event-recorder.js?token=<TOKEN>">
</script>

Then, the AI builds test cases based on the source code and users' interactions with the system. This process is much easier than designing and implementing your own automated test framework and provides the accuracy and test readability that QA engineers expect.

Test framework maintenance

Maintaining an automated test framework can be challenging as test scripts need to be updated as applications evolve. QA engineers must constantly tweak their tests to cater to new features and user flows introduced to the application under test.

AI maintains test cases by automatically updating them in response to changes such as new features or UI modifications, thus reducing the manual workload on engineering teams. The AI observes user activity and creates associated user journeys. These user journeys are evaluated and prioritized based on the user activity in that area. If the application changes and a particular user journey is no longer possible, it will deprecate automatically, with a new one taking its place. This ensures that the testing process stays aligned and remains current with the active state of the application at all times without requiring engineer intervention.

Regression test management

Regression tests ensure that code changes do not affect existing software functionality. They maintain software quality over time, identify unintended side effects early, and facilitate continuous improvement and integration processes. Regression tests act as a safety net that supports stable and reliable software development by consistently verifying that the application behaves as expected after each change.

AI testing tools also detect anomalies and predict potential areas of failure, enhancing the effectiveness of regression testing by focusing on high-risk areas. AI-driven tools learn from past test results, refining their test strategies to identify regressions more easily.

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Test coverage measurement

Test coverage measures how your testing suite evaluates the software's functionalities, guiding test quality and potential improvements. High test coverage ensures that more parts of the application are checked for defects, reducing the risk of bugs reaching production. It identifies untested parts of the codebase, allowing for more focused testing efforts and enhancing the software's overall reliability and security.

AI testing tools often provide highly visual dashboards showing exactly what part of the application is being tested. This ability to drill down into test results to understand the context of failures or anomalies goes beyond the static reports generated by traditional tools. Additionally, AI constantly creates new tests in response to user actions, automatically keeping test coverage high as the application evolves. 

Integration with existing tools

If the QA testing tool you deploy seamlessly integrates with your existing tools and workflow, it will dramatically reduce teams' learning curves by allowing them to work within familiar environments, increasing productivity and reducing the risk of errors.

For example, Qualiti, an AI-powered testing framework, offers integrations to ClickUp and Jira, two prevalent project management software tools. This allows you to automate an incredible range of tasks, such as updating ticket status based on test results or creating and assigning new tickets to QA engineers that contain error information that needs to be triaged.

Integration with the CI/CD pipeline

Integrating a QA testing tool into your CI/CD pipeline ensures that every code commit is automatically tested for issues, enabling earlier defect detection and faster resolution times. This seamless integration facilitates continuous testing, which aligns with continuous integration and delivery principles.

Some AI testing tools, like Qualiti, offer native support for GitHub and GitLab. This compatibility streamlines the development process and makes the overall deployment more efficient and reliable.  This integration allows AI testing tools to trigger tests upon code commits or pull requests automatically. New changes are immediately tested, reducing the time between code submission and feedback. Both developers and QA engineers will receive immediate feedback on their commits, allowing them to fix issues quickly.

Cross-platform and cross-browser testing

Cross-platform and cross-browser testing is crucial because it ensures that software applications provide a consistent user experience across different environments, including various operating systems, devices, and web browsers. This type of testing identifies compatibility issues, helping to prevent functionality failures or layout discrepancies that could negatively impact the user experience.

AI test tools dynamically adjust tests based on the unique behaviors of different browsers and operating systems, ensuring comprehensive coverage and efficient detection of layout and functional discrepancies.

UI testing

QA test frameworks should allow for comprehensive UI testing. Ideally, the framework facilitates the automation of these tests to ensure consistency and accuracy while identifying usability and design issues.

AI enhances UI testing by analyzing user interactions to identify and prioritize critical paths and high-usage functionalities. This approach focuses on testing areas of highest user engagement to improve application quality and usability. 

For example, imagine an e-commerce platform that sells various products, from electronics to clothing. AI examines user interactions within the platform's UI to optimize test prioritization across different sections. 

• If the electronics section shows high user activity, AI increases testing focus on this section to ensure the checkout process, search functionality, and product filters are working flawlessly. 
• It also identifies sections with lower user traffic, such as a seasonal items category, and scales down test efforts accordingly. 

By adjusting test coverage to match user engagement, AI guarantees that critical functionalities receive more attention, improving the overall application's quality.

Manage test results

Managing test results effectively involves organizing, analyzing, and visualizing the data to identify trends and issues quickly. Sufficient visualization, such as dashboards, charts, and screenshots, is crucial for interpreting test outcomes, particularly for distinguishing between successful and failed tests. 

The ability to view precisely what is being tested is vital for tests created by AI. Since QA engineers do not write these tests manually, they may be skeptical at first of what AI has produced. Allowing QA engineers and non-technical stakeholders to view these tests gives team members more confidence in AI and its ability to test the system comprehensively. 

Qualiti is an AI-powered testing framework that allows users to drill down and view specific test steps to see exactly what each auto-generated test is doing:

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Choose your QA tools with confidence

In choosing the right QA testing tools, it's crucial to weigh features like integration capabilities, maintenance burden, reporting capabilities, and cost alongside the innovative benefits offered by AI-driven tools. From enhancing automated test generation to refining maintenance and anomaly detection, AI significantly upgrades traditional frameworks. Whether you opt for open-source solutions like Selenium and Cypress or AI-enabled platforms like Qualiti, the right tool should align with your technical requirements and improve your testing workflow. Remember, the ultimate goal is to ensure high-quality software through efficient, comprehensive testing, and your chosen tool should reflect this.