How to Use AI in QA Testing: A Complete Guide

Traditional QA testing processes are time-consuming, error-prone, and less efficient, and they often involve repetitive manual work. However, this can be overcome by incorporating AI in QA testing, which empowers teams to enhance their testing process.

This approach to quality assurance involves using artificial intelligence, machine learning algorithms, deep learning models, and other AI testing tools to improve efficiency and speed while reducing the error factor caused by humans and minimizing costs.

In this blog, let’s look at how to use AI in QA testing.

What Is AI in Quality Assurance?

AI in quality assurance refers to using artificial intelligence technologies to enhance the process of testing software applications. It helps automate tasks that are usually time-consuming and repetitive, such as running and generating tests, identifying bugs, and analyzing results.

With AI, QA testing becomes faster, more accurate, and more efficient, reducing human errors and speeding up software release cycles. AI can also predict potential issues and suggest improvements, making the overall quality assurance process smarter and more effective.

Why Leverage AI in QA?

Traditional QA testing faces challenges such as slow testing cycles, human errors, and difficulty managing large test suites and test cases. These methods also struggle with scalability and adapting to constant software upgrades.

Additionally, identifying flaky tests and debugging can be time-consuming. Here is how leveraging AI can help enhance the QA testing process:

• Test Automation: Speeds up testing by automating repetitive tasks and executing tests faster.
• Smart Test Selection: Selects the most relevant tests, saving time and resources.
• Predictive Analysis: Anticipates potential issues and optimizes test coverage.
• Flaky Test Detection: Identifies unstable tests, making debugging easier and more efficient.
• Improved Accuracy: Reduces human errors by detecting defects more accurately.

Note

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Examples of AI in QA

According to the Future of Quality Assurance Report, there are several key examples that highlight how AI is used in testing processes.

• Test Data Generation: Creating diverse and comprehensive datasets is a critical part of QA, but it can be labor-intensive. AI automates this process, generating synthetic data that closely mimics real-world data. It not only saves time but also ensures that all possible scenarios are covered, making the tests more thorough and reliable.
• E2E Automated Test Code With AI: AI can generate automated E2E test scripts that simulate user interactions and validate their behavior in various conditions. This all-inclusive approach to testing ensures that everything works well together in unison, including all the components and integrations.

With AI-driven E2E testing, tests become more efficient and their coverage better, providing a more reliable evaluation of an application’s functionality.

• Unit Tests With AI Code Creators: Ensuring that individual software components function correctly is essential for overall quality. AI tools can automatically generate unit tests for code, covering various scenarios and edge cases. It not only saves time but also ensures that the code is thoroughly tested, leading to higher reliability and maintainability.
• Test Optimization and Prioritization: It’s a significant challenge to deal with too many test cases, but the most critical aspect here is prioritization based on risk and impact. The AI helps optimize and prioritize test cases through analysis of historical data to identify risky areas and focus more on high-priority tests.
• Scheduling and Orchestration: Efficient test execution requires proper scheduling and orchestration of test resources. AI assists in managing the execution of tests by optimizing the allocation of resources and scheduling tests at the right times.

This ensures that testing activities are completed on time and with minimal resource conflicts, improving the overall efficiency of the QA process.

• Visual Regression Testing: The visual aspect is crucial in software applications so any unintended change will directly impact the user experience. The AI-based visual regression testing is used to compare two versions of the software application in order to identify the visual discrepancy between the two versions.

For example, cloud-based testing platforms like LambdaTest lets you perform smart visual testing using its AI-powered SmartUI platform to detect visual deviations across different environments.

• Maintenance and Healing: Frequent changes in software applications can lead to broken test scripts, increasing maintenance efforts. The AI tools update and correct test scripts automatically whenever changes are made to the software application. This self-healing nature of the tool ensures that tests remain current and active so QA teams can focus on strategic work rather than spend time on manual script maintenance.
• Analytics and Reporting: AI-powered analytics also predict future problems based on historical data, so teams can address problems early before they become critical issues. Detailed reports that AI generates can be used to increase efficiency and effectiveness in the QA process.

Six Levels of AI in QA Testing

The six levels of AI in QA testing represent a spectrum of automation, from manual testing to fully AI-based testing. As AI capabilities evolve, they gradually reduce the reliance on manual intervention while improving testing efficiency and accuracy.

Here’s a more detailed look at each level:

1. Manual Testing: At this foundational level, human testers are fully responsible for making all decisions and handling every aspect of the testing process. They write and execute test cases, identify issues, and report bugs without the assistance of test automation. While manual testing is crucial for exploratory testing and complex scenarios, it is time-consuming and prone to human error.
2. Assisted Test Automation: At this level, automated testing tools are introduced to help testers but the writing and maintenance of test scripts are still performed by humans. Testers are heavily involved in the design, execution, and management of tests. AI plays a supporting role, assisting testers in executing tests more efficiently but without taking control of decision-making.
3. Partial Test Automation: This level represents a hybrid approach where both human testers and automated testing tools contribute to the testing process. While the tools assist with executing tests, most decisions regarding test execution, test data management, and result analysis are still made by human testers.
4. Integrated Automated Testing: In integrated automated testing, AI capabilities are introduced into automated testing tools, providing suggestions or insights that can help guide testers in refining test cases or optimizing test suites. Although human testers remain involved, their role shifts more toward approving and adapting suggestions provided by AI.
5. Intelligent Automated Testing: At this level, AI tools take a more active role in test generation, evolution, and execution. Testers’ involvement is optional but still possible when needed. AI can automatically generate test cases or scripts based on code changes, run tests and provide reports without requiring human intervention. This level significantly accelerates the testing process by automating much of the decision-making and evaluation. Testers may still intervene to address specific edge cases or scenarios that AI cannot yet fully handle, but AI plays a foundational role in the overall testing process.
6. Autonomous Testing: In autonomous testing, AI models take full control over the entire testing process, including decision-making, test creation, execution, and evaluation. Testers are no longer required, as AI handles everything autonomously. The tools are capable of continuously analyzing code changes, identifying potential risks, and running tests without any human input.

It is the ultimate level of AI integration, where AI autonomously tests, detects issues, and even proposes solutions. This makes it a highly efficient, scalable approach to QA. While human oversight may still be necessary in extremely complex cases, the goal is for AI to handle the bulk of testing without human intervention.

How to Use AI in QA Testing?

Using AI in QA testing involves a systematic approach to ensure accuracy, efficiency, and scalability. Here are the five essential steps that outline how to use AI in the QA testing process:

1. Identify Test Scope: Focus on identifying the scope and objectives of implementing AI in QA. Define the key areas for using AI in different areas, such as improving test coverage or automating repetitive tasks.
2. Select AI Models: Now select the AI models that best fit your software project requirements. For example, if you want to automate the test generation process, you can choose an NLP-based AI model to generate tests.
3. Train AI Models: High-quality data is essential for training AI models. Collect, curate, and label the data needed for training AI models. Also, use the proper data annotation method to ensure that the AI model can recognize patterns, execute accurate tests and predict defects.
4. Validate AI Models: Once the AI model is trained, test and validate it. Develop test algorithms and evaluate models using subsets of the annotated data. The goal is to verify that the model performs as expected in real-world scenarios by producing accurate and consistent results. This phase often involves iterative testing to refine the model based on observed behaviors.
5. Integrate AI Models Into Your Workflow: Once the AI model is tested and validated, integrate it into your testing infrastructure. This can involve automating aspects of the testing process, like generating test cases or analyzing test results.

While it’s possible to build and train custom AI models for QA testing, it’s often more efficient to leverage AI testing tools that come with native or embedded AI features and can be integrated with your workflow. Using these tools can save your time, reduce complexity, and streamline your QA testing process, especially if you’re focused on speeding up test execution without worrying about training the models extensively.

AI Tools for Quality Assurance

AI-driven tools are reshaping software testing by automating key processes and enhancing efficiency. From generating intelligent test cases to optimizing reporting and analytics, these tools simplify QA workflows, reduce errors, and improve accuracy.

The following section covers some leading AI testing tools for QA teams, highlighting their unique features.

KaneAI

KaneAI by LambdaTest is an AI-powered test assistant that streamlines test authoring, management, and debugging for high-speed quality engineering teams. It simplifies the creation of automated tests using natural language processing, making automation accessible for teams with varying technical expertise.

Features:

• Intelligent Test Generation: Automates the creation and evolution of test cases through NLP-driven instructions.
• Smart Test Planning: Converts high-level objectives into detailed, automated test plans.
• Multi-Language Code Export: Generates tests compatible with various programming languages and frameworks.
• Show-Me Mode: Simplifies debugging by converting user actions into natural language instructions for improved reliability.

Katalon Studio

Katalon Studio is an all-in-one test automation platform offering AI-driven features for web, mobile, desktop, and API testing. Its codeless automation and self-healing mechanisms improve test reliability and reduce maintenance efforts.

Features:

• AI-Powered Test Object Recognition: Identifies and adapts to UI changes, minimizing test script failures.
• Data-Driven Testing: Supports diverse data sets to validate software behavior across scenarios.
• Self-Healing Tests: Automatically updates scripts when applications evolve.
• Built-In CI/CD Integrations: Enables seamless integration with DevOps workflows.

Aqua Cloud

Aqua Cloud provides intelligent test management solutions, leveraging AI for test planning and test optimization. It centralizes testing workflows and offers predictive analytics to enhance decision-making.

Features:

• Test Management Automation: Reduces manual overhead with AI-driven workflows.
• Collaboration Tools: Supports cross-functional QA and development team collaboration.
• Scalability: Handles extensive testing needs across large software ecosystems.
• Analytics and Reporting: Provides actionable insights through predictive data analysis.

Functionize

Functionize combines AI and machine learning to deliver smarter test creation and execution. It supports natural language-based test scripting, making it user-friendly for non-technical teams.

Features:

• Natural Language Testing: Allows test creation using simple, human-readable instructions.
• Cross-Browser Compatibility: Ensures robust testing across various platforms.
• Self-Healing Tests: Maintains test integrity even with UI or code changes.
• Cloud-Based Execution: Offers scalability and high-speed test execution in the cloud.

Demo: Using KaneAI for AI QA Testing

Let’s look at how you can perform AI QA testing with KaneAI. For demonstration, we will test

Note: To get access to KaneAI, please contact sales.

1. From the LambdaTest dashboard, click the KaneAI option.



2. Click on the Create a Web Test button. It will launch the virtual environment with a side panel available to write test cases.



3. In the Write a step textarea, enter your test steps one-by-one and press the Enter button. For this demo, follow these steps:
   1. Visit the URL www.lambdatest.com
   2. Click on Resources
   3. Click on Blog

KaneAI will generate the test cases based on your test steps:



4. Click on the Finish Test button and then Select Folder to locate your test cases. Then, click on the Save Test Case button to save them.

It will now redirect you to the screen below, where you can select and use different options according to your requirements.

To get started, refer to this guide on KaneAI.

Future Trends of Using AI in QA

AI is poised to bring transformations to the QA process, driving efficiency, accuracy, and innovation. Here are key trends shaping the future of AI in QA testing:

• Test Case Generation and Authoring: AI models can analyze historical testing data to generate test cases aligned with an organization’s specific needs. Over time, these models may independently generate complete test cases, reducing manual effort. However, for such models to be effective, continuous feeding of diverse and extensive testing data is essential to enhance AI learning.
• Test Environment Setup: AI can revolutionize the time-intensive task of setting up a test environment. By leveraging data on system performance, configurations, and suggested test cases, AI can automate environment setup, ensuring faster and more accurate configurations tailored to the test scenarios.
• Test Orchestration: AI has the potential to manage end-to-end testing processes autonomously. By accessing real-time data on testing resources, it can schedule and run tests based on resource availability, prioritizing tests dynamically in distributed and complex environments. This level of orchestration ensures optimal utilization of testing resources and reduces testing cycle times.
• Visual Testing: AI-based visual testing can identify visual anomalies, reducing false positives and ensuring a more reliable user experience. This approach helps pinpoint visual bugs that can impact the usability and accessibility of software applications.

Conclusion

AI is improving QA by providing advanced solutions that improve speed, accuracy, and efficiency in software testing. From automating test case creation to predicting defects and generating test data, AI helps streamline the testing process and ensures higher-quality outcomes. With different AI tools, as mentioned, AI-driven testing is becoming a critical component of modern QA strategies.

By using AI in QA practices, organizations can accelerate release cycles, reduce testing costs, and enhance product quality. Adopting AI QA is a smart approach to maintaining competitiveness and ensuring robust software performance.

Zikra Mohammadi

Zikra Mohammadi is a skilled technical content writer and strategist with more than five years of experience in web development and testing. As a freelancer, she is passionate about transforming complex technical topics into clear, engaging, and impactful content. Whether it’s blog posts, website content, case studies, or product descriptions, she brings a strategic approach to every project.

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