What is AI Testing: A Complete Guide

What Is AI Testing?

AI testing is the use of artificial intelligence to plan, write, run, and maintain software tests, for example generating test cases from a natural language prompt or repairing a locator after the UI changes. The term is also used for testing AI systems themselves, such as models and LLM apps; this guide covers the first meaning.

The two meanings are different jobs. The ISTQB Certified Tester AI Testing (CT-AI) certification covers testing AI-based systems, whose probabilistic behavior and reliance on data need their own test design, and ISTQB points testers who want to use generative AI in their own testing to a separate certification, CT-GenAI.

Adoption for test work is already measurable. In the Stack Overflow 2025 Developer Survey, 17.9% of respondents said they already use AI for most of their testing-code work, slightly more than the 16.9% who said the same for writing code.

AI-driven testing does not have to cover the whole cycle. A team can hand AI one stage, such as test authoring, and keep execution and review as they are, or let an agent plan, author, run, and maintain tests with little manual scripting.

Types of AI Testing

Each type of AI testing is defined by the job the AI takes over:

• AI-powered test generation: AI creates test cases and scripts from requirements or app behavior, often from plain-language instructions.
• Self-healing test automation: AI updates broken locators automatically when the UI changes, cutting test maintenance.
• Visual AI testing: AI compares screens to catch visual and layout regressions that assertion-based checks miss.
• Predictive, risk-based testing: AI analyzes code changes and past failures to prioritize the tests most likely to find defects.
• Flaky-test detection and root-cause analysis: AI flags unstable tests and traces failures to their underlying cause.
• Agentic testing: Autonomous AI agents plan, author, run, and adapt tests end to end with minimal human input.

For how AI handles the full automation cycle, see the guide to AI test automation.

Why Use AI in Software Testing?

Teams use AI in testing to cut the manual work in authoring and maintaining tests, so the same people can cover more of the application. Each stage of the testing process gains something different:

• Test planning: Analyzes historical test data to optimize test coverage and prioritize test cases based on risk and complexity.
• Test data generation: Automates the creation and management of test data, reducing manual input and ensuring more accurate testing.
• Test case creation: Helps quickly generate test cases by learning from user interactions and previous test executions.
• Test authoring: Simplifies test authoring by offering suggestions and automatically writing tests based on application behavior.
• No-code test automation: Enables testers to automate tests without coding by using visual interfaces and intuitive workflows.
• Auto-healing: Detects changes in the application and automatically updates test scripts, minimizing the need for manual adjustments.
• Flaky test detection: Identifies and flags flaky tests by analyzing patterns in test execution, improving test reliability.
• Root-cause analysis (RCA): Helps identify issues by analyzing test results and tracing failures to their underlying causes.

For the analysis side of this list, the TestMu AI Test Intelligence platform applies flaky test detection, auto-healing, and root-cause analysis to your existing test runs, so you can see which failures are real before anyone triages them.

Note: Run AI-generated tests across 3,000+ browser/OS combinations with TestMu AI. Try TestMu AI free!

How to Start AI Testing

Start AI testing by picking one testing task for AI to take over, choosing the AI technique that fits it, and checking the AI's output before you rely on it. The steps below follow that order.

• Define clear objectives: AI testing does not have to be fully autonomous. It can be included as part of a phase where it carries the heavier load and leaves minimum manual work for the testers. To achieve this, the team should be clear about the objectives they wish to achieve by introducing AI in their infrastructure.

For instance, some teams lack resources, and therefore, their main objective is to get the scripting work done through AI. Such clear objectives help the team decide which tool is best and which technology they wish to adopt (such as predictive analytics and NLP).

• Choose the AI technique: The objectives defined in the previous step will carve the path to select AI tools to include them in the cycle and perform AI testing.

For instance, in the previous example, the team had to adopt AI due to a lack of resources, and their objective was to get help in writing test cases. In such a scenario, an ideal technology to adopt is natural language processing, through which the test cases can be written in English and be interpreted by NLP models.

• Train algorithms: Once the team has the technology, they need to train it on their organization's data so that the algorithm understands the requirements and produces output only relative to this training data. It is an extremely important step and, if possible, should be done by an AI expert.
• Measure the efficiency and accuracy: The trained AI model associated with the algorithm is not foolproof evidence that the model will work as expected. It is because the team has no proof of its accuracy and efficiency except in theory. Therefore, before feeding, the algorithm must be tested through AI testing techniques.

For this, below are some AI testing techniques you can use to test an AI algorithm:

• Model interpretability testing: It verifies the model's output and decisions, especially when compared to the current software project in which it will be integrated. It helps make sure the outputs are correct and builds trust among the stakeholders about the adoption of the algorithm.
• Bias and fairness testing: It ensures that the algorithm is not biased toward any parameter of the input and the algorithm provides fair and impartial output.
• Data Quality and Validation Testing: It ensures that the data quality provided by the AI algorithm is high. Most of the time it means that the data generated is accurate and covers all the scenarios and corner cases for an enhanced test coverage.
• Adversarial Testing: It ensures the negative inputs do not break the algorithm and make their way out as errors or incorrect inputs. This includes providing wrong and malicious inputs that are either not expected or do not fit with the input criteria of that particular algorithm.
• Black-Box Testing: It tests the output by providing some input to the algorithm but not considering the internal structure or how it works.
• White-Box Testing: It tests the underlying code, the working of the algorithm, and all the other complexities. The inputs provided to the algorithm in this testing are based on all these understandings to cover the hidden and complex cases.
• Integrate With the Test Infrastructure: The tested AI model can then be integrated into the test infrastructure at specific places to conduct smoother AI testing.

AI-Driven Strategies for Software Testing

Beyond test generation and self-healing, AI also changes how exploratory, visual, continuous, performance, and security testing are run:

• Exploratory Testing: AI assists exploratory testing by suggesting test scenarios based on user behavior, analyzing interactions, and recommending new areas to explore automatically.
• Visual testing: AI-native tools compare screenshots and detect UI changes with image recognition. For example, TestMu AI SmartUI runs a visual testing tool across browsers and devices.
• Continuous Testing: AI optimizes continuous testing by prioritizing tests, predicting failures, and adapting test cases based on real-time data, improving automation pipelines in CI/CD environments.
• Performance Testing: AI can simulate real-world user behavior and analyze performance under various conditions, predicting performance bottlenecks and suggesting improvements.
• Security Testing: AI can be used to autonomously identify vulnerabilities, predict threats, and analyze security risks, improving the speed and scope of security testing.

To see these strategies applied, watch the video walkthrough of AI use cases in software testing.

Tools Used for AI Testing

Pick an AI testing tool by the job you want AI to take over first: natural-language authoring, enterprise model-based automation, or test-idea drafting inside the browser. The table compares the four tools covered below on that basis; for a longer list, see the roundup of AI testing tools. For approval gates, access controls and a pilot plan across many teams, see the guide to enterprise AI testing.

TestMu AI KaneAI

KaneAI is a GenAI-native testing agent from TestMu AI that plans, authors, runs, and maintains tests from natural language prompts. It accepts a typed prompt, a Jira ticket, a PRD, or a recorded session as input and turns it into executable steps with assertions.

Key Features of KaneAI

• Intelligent test generation: Creates and updates tests from natural-language instructions.
• Intelligent test planner: Automatically generate and automate test steps using high-level objectives.
• Multi-framework code export: Exports tests to Playwright, Selenium, Cypress, or Appium code, so the team keeps its own codebase.
• Conditional logic: Expresses If/Else conditions, loops, and assertions in natural language.
• API testing support: Adds backend API checks to the same flow as the UI steps.
• Device coverage: Runs generated tests across 3,000+ browser/OS combinations and 10,000+ real devices.
• Self-maintenance: Auto-Heal recovers a broken locator during the run, and a separate setting decides what happens to a step it cannot recover, either re-authoring the failing objective or authoring every objective from its goal on each run. The result waits in Version History for approval.
• Evidence Reporting: A fuller run report covering coverage details and the business use cases behind the tests, available where every test case in the run was authored with the New Experience engine and the run executes on Chrome.

TestCraft

TestCraft is a free, open-source browser extension that uses OpenAI models to draft test ideas, automation scripts, and accessibility checks from the elements on the current page.

Features:

• Automated test creation: Generates scripts for Playwright, Selenium, or Cypress in JavaScript, TypeScript, Python, C#, or Java.
• Generate Ideas: Generates ideas and scenarios for the testing phase that help increase the test coverage.
• Accessibility checks: Generates accessibility test cases and can provide suggestions for accessibility issues on the existing test cases.

Tricentis Tosca

Tricentis Tosca is an enterprise test automation tool that combines model-based testing with agentic AI, aimed at packaged applications such as SAP, Oracle, and Salesforce.

Features:

• Model-based test automation: Divides the application's view into various smaller units called models. Everything that a tester does is then connected to these models.
• Agentic AI: Uses AI agents alongside the model-based approach, with unattended parallel execution on the cloud.
• Automatic Test Case Conversion: Record the actions and convert them into test cases.

Functionize

Functionize is an AI test automation platform where you describe what to test, and it builds the test, runs it, and keeps it passing as the application changes.

Features:

• NLP Test Creation: Allows testers to define test steps with natural language prompts, which are automatically translated into executable test scripts.
• Self-Healing Tests: Uses ML models to adapt tests when the UI changes, reducing the need for manual test maintenance.

How to Run AI Testing With KaneAI

To run an AI test in KaneAI, write each step as a natural language prompt in the test session, let the agent execute it in a live browser, and save the recorded steps as a reusable test case. A testing agent avoids the setup and model-tuning work of building your own AI, which is why it suits teams starting out.

The walkthrough below checks whether the video on the Real Device Cloud page plays correctly.

• From the TestMu AI dashboard, click the KaneAI option.

• Click on the Create a Web Test button. It will open up the browser with a side panel available to write test cases.


Shown below is the snap of the test session, where you can either write the test steps or interact with the browser agent manually. In the latter case, the actions will be recorded and repeated any number of times.


• In the Write a step text area, write "go to https://www.testmuai.com" and press Enter to visit the website.

• As soon as we press Enter, the test step is recorded, and the website is opened on the browser. We can repeat the same process now with new test steps. These steps can be as follows:
• Scroll down
• Click on real device cloud
• Click on more about real device
• Click on video
• Click X

Writing these steps one by one will execute them on KaneAI. These steps are recorded and can be reused later.


• Click on the Finish Test button at the top right to finish this testing session.


The next screen will ask to save the test case. You can notice how fields such as Test Name and Description are already populated by AI, therefore cutting down the time a tester has to spend on these things. Here, the tests can be saved in a folder and reused later when required.

If your team works from the terminal, or you want an AI coding agent such as Claude Code or Cursor to check its own changes, Kane CLI runs the same kind of natural language prompts from the command line in a real Chrome browser and returns a pass or fail result that a CI pipeline can act on.

Challenges in AI Testing

The hardest part of AI testing is trusting the output: an AI-generated test or repair can pass while the feature is broken, and the same input can give different results.

• Verification of AI Algorithms: AI algorithms work mostly with predefined libraries and functions included in the package. The algorithm may be easy to incorporate, but the accuracy of it cannot be determined easily. However, various AI testing techniques are available to accomplish this, but a team may never be able to compare the actual output with algorithms.
• Unpredictability of Algorithms: AI algorithms are sometimes unpredictable as they provide different outputs for the same input. This can lead to doubts over the outputs of the subsequent methods used after the AI algorithms.
• Good Training Dataset: An AI tool may have been trained using a poor dataset where biases might exist. Such a dataset may present a biased output, making the software application biased towards a certain parameter.
• Repairs That Hide Regressions: When an agent fixes a failing test instead of reporting it, a green run stops being evidence. A repair that re-points a step at a similar control, or weakens the assertion that was catching a defect, leaves the suite passing while the feature is broken. The signal worth tracking is how many proposed repairs a reviewer declines, not how many runs went green.
• Integration Hurdles: AI testing tools are new and run with little human input, so integrations with third-party tools beyond CI/CD can lag, and testers may have to wait before the full flow connects.

Best Practices to Follow in AI Testing

Treat AI output as untrusted until it is checked: test the algorithm on your own data, gate automatic repairs, and keep the AI's datasets clean.

• Test the Algorithm First: While adopting a tool or algorithm to integrate into the application, plenty of resources might confirm its behavior and the most suitable environment in which it works. Still, it is always a good practice to test the algorithm according to your project using the data that is generated and used by the application. A tested algorithm is the safest starting point.
• Collaboration With Other Tools: Not every AI tool can conduct complete end-to-end testing without any manual efforts. Sometimes, they handle only a part of the application, such as UI and more.

Therefore, it is a good practice to know the limitations of the AI tool the team is using and collaborate with other tools to create a unified structure. Depending on the AI tool completely can have serious concerns in the future.

• Gate Automatic Repairs: Let the agent propose a fix, but keep a person on the approval step wherever a wrong repair is expensive. In KaneAI, Auto-Heal recovers a broken locator during the run, while the Self-maintenance setting that re-authors a failing step ships off and holds its result as a draft version that becomes current only after approval, as the documentation on healing and dynamic test sets out. Record which repairs get declined so the gate stays measurable.
• Avoid Security Loopholes: AI testing requires changes in a lot of processes and involves third-party software or an algorithm taken from known or unknown sources. In such a case, security loopholes can occur, making the setup vulnerable to security threats.

An unsecured network is probably the last thing any business would want, as it raises a lot of legal issues. Therefore, before integrating, make sure the setup is secure by either inviting security engineers or cyber security experts who have expertise in this field.

• Sustain High-Quality Datasets: A lot of testing processes require data and AI algorithms are the ones that generate it during the execution or before it in the preparation phase. It is important that a quality check is performed on this dataset to ensure the test execution is done in a high-quality phase.

It can be done by verifying the algorithm accuracy that generates data or on the go through an attached algorithm. It can also be done manually, but that can break the autonomous testing flow if that is the end goal.

Conclusion

Pick one test flow your team rewrites most often, describe it with natural language prompts in KaneAI using the KaneAI getting started guide, and compare the generated test with your hand-written one before you hand AI any more of the cycle. Keep a reviewer on every automatic repair.

For the wider picture, read how AI automation and generative AI in testing fit into the rest of the QA process.