How to Generate Test Cases With AI

Why Use AI to Generate Test Cases?

Incorporating AI to generate test cases provides consequential benefits and eases your test case creation process:

• Intelligent Test Case Generation: AI leverages project requirements, user behavior, and historical test data to generate test cases based on them. It ensures test cases align with real-world use cases and offers better insights into potential edge cases. This also opens the door for focused capabilities like AI unit test generation, where AI can create unit-level test cases to validate individual components efficiently.
• Enhanced Test Coverage: Test coverage and complexity do not go hand in hand. If testers try to increase their test coverage, they create more complex test cases, which will take a significant amount of time to maintain in the future. Hence, test coverage will always suffer when test cases are written manually. However, AI-based test cases need not be maintained. If so, AI testing tools automatically self-heal them. Therefore, AI can focus on test coverage instead of complexity, as tests will not require manual intervention.
• Enhanced Performance: When AI generates test cases, it considers parameters besides functionality. For instance, user behavior helps write test cases that validate functionality based on the end user’s behaviors and actions.
• Defect Prediction: AI tools monitor test execution to identify anomalies and predict potential defects. Over time, AI refines its predictions by learning from past test results, enabling proactive defect prevention.
• Learn From Experience: The AI model evolves by analyzing historical data and previous test results. It generates smarter, more adaptive test cases that address known issues and improve with each iteration, ensuring relevance over time.
• Time Saver: AI algorithms require just the context of the test cases (often through textual input boxes) and generate test cases within a couple of seconds. Hence, generating test cases with AI saves a lot of time.

To generate test cases using AI, the testing team must adopt at least one tool and integrate it into their infrastructure. Since AI is a vast area with different tools providing different capabilities, it is important to know what types of tools exist in the market, what they offer, and what will suit the best in our existing infrastructure for automatic test case generation.

This can be demonstrated here with three completely different AI tools that help you generate test cases in different ways.

Note: Generate your AI-Native test cases. Try TestMu AI Today!

Generating Test Cases With TestMu AI Test Management

TestMu AI's test management platform is an AI-Native solution with an integrated test case authoring and execution facility. It centralizes all test case-related information, making it a unified platform for managing tests and related activities.

Features:

• Generates test plans and test cases using natural language for better organization.
• Creates behavior-driven test scenarios from automation logs.
• Monitors all test runs from a centralized dashboard.
• Imports test cases via CSV or API with auto-mapping.
• Comes with quick keyboard shortcuts like Tab to auto-generate test cases.
• Enables the team to migrate effortlessly from legacy test management tools.
• Integrates seamlessly with tools like Jira for real-time bug tracking.

Let’s look at how to generate test cases with AI using TestMu AI:

Note: Ensure you have access to the Test Management. If not, please contact sales.

• Once you are logged in to the TestMu AI Home dashboard, click on the Test Manager option from the side panel.

• Create a new project and start from scratch.
• Import the test data from existing test management tools.

• For this demonstration, we will create a new project from scratch by clicking on the Create Project button.

• Fill in the project details on the side panel that appears and click on the Create button.

• Click on the project name (in this case, MyFirstProject) to start creating test cases:

• Add a test case title manually and press the Enter key to add and save it.

• Click on the test case title after saving it.

• Add test steps in natural language. Here, too, you can press the Tab key to generate the test case automatically.

TestMu AI provides a detailed view for organizing test case information. It allows you to input the following details such as:

• Test Case Details: Enter relevant information about the test case.
• Status: Specify the current status of the test case.
• Description: Provide a clear description of the test case.
• Pre-condition: List any necessary pre-conditions for the test.
• Type: Define the type of test case.
• Priority: Assign a priority level for the test case.
• Automation Status: Indicate if the test is automated or manual.
• Tags: Add tags to categorize the test case.
• Attachments: Attach relevant files to the test case.

To get started, head over to this documentation on Introduction to Test Manager.

TestMu AI Test Management also provides an option to generate tests from the above-created test cases with its very own AI agent for QA testing, KaneAI. You can simply click on the Automate with KaneAI button to generate these tests seamlessly.

KaneAI by TestMu AI is a GenAI native QA Agent-as-a-Service platform for high-speed quality engineering teams. It allows you to create, evolve and debug tests using natural language. KaneAI also seamlessly integrates with the rest of TestMu AI’s offerings for test orchestration, execution and analysis.

With the rise of AI app testing, its crucial to stay competitive by upskilling or polishing your skillsets. The KaneAI Certification proves your hands-on AI testing skills and positions you as a future-ready, high-value QA professional.

Want to get a sneak peak of KaneAI? Watch the video below:

Subscribe to the TestMu AI YouTube Channel for more videos on AI testing concepts.

Key Takeaway: TestMu AI Test Management authors test cases in natural language and runs them from the same platform, with CSV or API import, auto-mapping, and Jira integration for bug tracking. Pressing Tab drafts a case with AI, and each case carries status, priority, type, tags, and attachments. The Automate with KaneAI button converts saved cases into automated tests.

Generating Test Cases With ChatGPT

ChatGPT is a Generative AI platform that takes input in textual form (or images through other branched tools) and generates output accordingly. Its popularity started as simple query-based software, but since then, it has grown to be used in software testing and development as well. All a developer needs to do is provide the query for which code can be generated.

For instance, you can enter the prompt Write test cases to test whether Java page is working or not in ChatGPT:

You can also use ChatGPT to generate test data for an input field. For example, you can enter the prompt Generate test data to test whether Java page is working or not.

Using ChatGPT to generate test cases can be highly beneficial, though some shortcomings should be considered. First, integrating the generated test cases into existing suites may require adjustments to align with the test environment, including variables, libraries, and network configurations.

Second, ChatGPT doesn’t offer specialized testing features like those found in dedicated testing platforms such as TestMu AI, which provides test execution environments and virtual browsers.

Lastly, while ChatGPT doesn’t include a built-in secure testing infrastructure, it’s still an excellent resource for generating test case ideas, exploring concepts, and getting code references.

Generating Test Cases With Claude

Claude is a fine-tuned Generative AI chatbot that can perform all the tasks of ChatGPT but also includes other dimensions. Its model is tuned for logical reasoning, including programming and mathematics. Claude works by taking an input (similar to ChatGPT), analyzing it, and then providing output that can vary from simple query answers to summarizing the documents and analyzing the code.

With respect to AI in software testing, let’s send a query on the platform to generate test cases using the same prompt we used above, i.e., Write test cases to test whether Java page is working or not in ChatGPT:

As it is clear, there is a stark difference between ChatGPT and Claude for the same query. Since Claude is tuned for programming, it presents a file with the code. The code appears on a different side panel with various colors, just like it would on an IDE. It helps enhance the readability, and the left panel summarises the code for better understanding.

Both ChatGPT and Claude.ai offer valuable capabilities, but they share some similarities in that they are independent chatbots not integrated into any specific system and work through their web applications. While the community often prefers Claude for its free version and ChatGPT for the paid version, these preferences are mainly based on output quality rather than integration or security concerns.

When it comes to AI testing, TestMu AI Test Management is the ideal choice. It provides a dedicated environment, infrastructure, and seamless integration.

With AI tools like ChatGPT, testers can now generate test cases, user flows, and even complete test strategies within minutes. If you’re looking for effective ways of using ChatGPT for test automation, this guide provides ready-to-use examples. You can also check out our KaneAI ChatGPT for testers page to see how these capabilities translate into practical, real-world testing applications.

Conclusion

Software testing has evolved along with other technologies, and its latest modification is the integration of artificial intelligence. AI has helped software testers generate test cases faster without any programming language, reduce maintenance burden with self-healing, and fix debugging errors automatically. You can even build out your manual test cases with AI and refine them from there.

This can be made possible by using an AI-Native platform such as TestMu AI.

For legacy systems where everything has been done manually, chatbots like Claude and ChatGPT may prove beneficial to generate code for reference. However, if the team does not want to invest too much time in writing code and maintaining it, integrated AI tools like TestMu AI Test Management are better. They generate test cases in English and provide an infrastructure to conduct testing as well.

However, all of the mentioned software are finely tuned and efficient in their goal, and in the end, all of them will cut down time and costs of the testing, which is what every organization wants.

Citations

• Research on Automatic Generation of Test Case: https://ieeexplore.ieee.org/document/6211132