What is AI Testing? A Complete Guide

Use Case of AI For Testing

According to the State of Software Quality Report 2024:

• AI is most commonly applied for test case generation, both in manual testing (50% respondents agreed) and automation testing (37%).
• Test data generation follows closely, with 36%.
• Test optimization and prioritization is another noted use case, at 27%.

Benefits of AI Testing

• Faster test execution
• Reduced manual effort
• Improved test coverage
• Self-healing automation
• Early defect detection
• Smarter test case generation
• Enhanced accuracy and reliability
• Predictive defect analytics
• Cost savings in long-term testing
• Continuous testing in CI/CD pipelines

Challenges of AI Testing

• High dependency on quality data
• Difficulty in explaining AI-driven decisions
• Not a full replacement for human testers
• Initial setup and training complexity
• Risk of biased AI models
• Requires continuous learning and updates

Is AI Going To Replace Testers?

The age-old question: will AI testing replace traditional software testers?

AI is indeed disruptive, and similar to many disruptive inventions in the past, it always create a sense of uncertainty and skepticism among its adopters.

AI technology is only in its infancy, but at the current rate this tech is growing, it is undeniable that it will affect the lives of so many people, including software testers. 

What testers need to do is adapt instead of panic.

A good way to think about it is to remember what AI can and can't do:

What AI Can Do:

• Automate regression, functional, and load testing.
• Identify patterns, anomalies, and defects faster than humans.
• Optimize test case selection and execution based on risk analysis.
• Self-heal test scripts to reduce maintenance effort.

What AI Can’t Do:

• Perform exploratory and usability testing, which require human intuition.
• Assess user experience, accessibility, and emotional responses.
• Make ethical decisions when evaluating bias and fairness in software.
• Understand business logic, edge cases, and subjective requirements beyond historical data.

In fact, in the age of AI, human ingenuity and creativity is more needed than ever. What testers need to do is:

• Learn AI-powered testing tools and frameworks.
• Shift towards test strategy, analysis, and automation oversight.
• Develop skills in AI ethics, interpretability, and human-AI collaboration.
• Adapt to a hybrid model, where AI handles repetitive tasks, and humans focus on critical thinking and decision-making.

Best Practices For AI Testing

1. Monitor AI Model Behavior – Continuously track performance to detect drift or unexpected changes.
2. Test for Bias & Fairness – Identify and eliminate biases in AI models to ensure ethical outcomes.
3. Perform Robustness Testing – Validate AI’s ability to handle edge cases and adversarial inputs.
4. Ensure Explainability – Use techniques to make AI decisions transparent and interpretable.
5. Continuously Improve – Update tests as AI models evolve, ensuring long-term accuracy and reliability.

Testing For AI Systems

The "AI testing" term can also be understood as testing for AI-based systems, or “testing for AI”. To process a tremendous amount of data to recognize patterns and make intelligent decisions, these AI systems incorporate many AI techniques, including:

• Machine learning
• Natural language processing (NLP)
• Computer vision
• Deep learning
• Expert systems

AI-Powered Tools for AI Testing

The following software testing tools pioneer the AI testing trend and incorporate AI technologies into their systems to bring software testing to the next level. More than simply a tool to create and automate testing, they also perform intelligent tasks that in the past would have required a human tester.

1. Katalon Studio

Katalon Studio is a comprehensive quality management platform that supports test creation, management, execution, maintenance, and reporting for web, API, and mobile applications across a wide variety of environments, all in one place, with minimal engineering and programming skill requirements.

For AI testing specifically, here are the key features you can have:

• StudioAssist: Leverages ChatGPT to autonomously generate test scripts from a plain language input and quickly explains test scripts for all stakeholders to understand.
• Katalon GPT-powered manual test case generator: Integrates with JIRA, reads the ticket’s description, extracts relevant information about software testing requirements, and outputs a set of comprehensive manual test cases tailored to the described test scenario.
• SmartWait: Automatically waits until all necessary elements are present on screen before continuing with the test.
• Self-healing: Automatically fixes broken element locators and uses those new locators in following test runs, reducing maintenance overhead.
• Visual testing: Indicates if a screenshot will be taken during test execution, then assesses the outcomes using Katalon TestOps. AI is used to identify significant alterations in UI layout and text content, minimizing false positive results and focusing on meaningful changes for human users.
• Test failure analysis: Automatically classifies failed test cases based on the underlying cause and suggests appropriate actions.
• Test flakiness: Understands the pattern of status changes from a test execution history and calculates the test's flakiness.
• Image locator for web and mobile app tests: Finds UI elements based on their visual appearance instead of relying on object attributes.
• Web service anomalies detection (TestOps): Identifies APIs with abnormal performance.

As one of the pioneers in the AI testing world, Katalon continues to add more exciting AI-powered features to their product portfolio, empowering QA teams around the world to test with unparalleled accuracy and efficiency.

2. TestCraft

TestCraft simplifies regression testing and web monitoring using AI and Selenium, reducing maintenance time and costs.

Key Features:

• No coding required – Drag-and-drop interface for easy test creation.
• Cross-browser testing – Run tests on multiple environments simultaneously.
• On-the-Fly mode – Automatically generates test models for easy reuse.
• AI-powered element detection – Identifies web elements even with UI changes.
• Adaptive testing – Adjusts to dynamic changes, minimizing test breakages.

3. Applitools

Applitools is a software that manages visual applications and employs visual AI for AI-powered visual UI testing and monitoring. The incorporated AI and machine learning algorithms are fully adaptive, enabling it to scan and analyze app screens like the human eye and brain, but with the capabilities of a machine.
 

Key features:

• It effectively identifies visual bugs in apps, ensuring that no visual elements overlap, remain invisible, go off-page, or introduce unexpected features. Traditional functional tests fall short in achieving these objectives.
• Applitools Eyes accurately detects material differences and distinguishes between relevant and irrelevant ones.
• Automation suites sync with rapid application changes.
• Cross-browser testing is supported, but with limited AI features.

4. Testim Automate