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Generate tests from a defined behavior contract, then review and run every result in the project’s existing workflow. Developers usually mean executable unit or integration test files; QA teams may mean manual test cases and steps derived from requirements. Those are different outputs, and neither is proof that the software is correct.
Choose the kind of test you need
Start by deciding whether you need tests that run against code or cases that guide a person through a requirement. Tools may support one or both, but the outputs serve different purposes.
| Output | Starting point | What you get | Typical use |
|---|---|---|---|
| Executable code tests | Source code plus expected behavior and project conventions | Test code in the project’s language and framework | Unit, integration, or end-to-end checks run with the project’s test tools |
| Requirement-based QA cases | A requirement, work item, or specification | Test cases, preconditions, and steps for review | Manual QA planning or a test-management workflow |
For example, Visual Studio Code documents Copilot-assisted generation of unit, integration, and end-to-end tests, as well as running and debugging tests in the editor. Katalon documents generating test cases and steps from requirements. These examples describe product capabilities, not a neutral ranking of quality or accuracy.
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1. Find the project’s existing test conventions
Before asking a tool to write a file, inspect the repository. Identify the test runner and framework, the test directory, file naming, setup and teardown, fixtures, mocks, and commands for running one test and the related suite. Read a nearby representative test. Reusing the project’s setup avoids introducing a second framework or a file the runner will not discover.
If the project has no test setup, propose and review a minimal one before generating test files. Microsoft’s Visual Studio Code testing guidance recommends using project-specific testing instructions and an established workflow.
2. Define behavior from the contract
Choose one small function, class, or module. Provide its relevant source, the requirement or documented behavior, and a representative existing test. Tell the generator the framework, target location, naming convention, fixtures, and mocking approach.
Use the specification—not just the implementation—to decide expected results. Microsoft warns in its test-generation guidance: “Asking the agent to infer every expectation from the implementation risks generating tests that preserve an existing bug.”
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Ask for cases tied to the contract, such as normal inputs, boundaries, invalid inputs, errors, and relevant side effects. Include only behavior the requirement defines; if an edge case is unspecified, clarify it rather than allowing a guess to become an asserted expectation. Keep tests independent and check observable outcomes.
GitHub’s prompt guidance recommends supplying the target function and framework and includes a sample prompt asking for 5–8 cases. That range is an example in a prompt, not a universal quality target.
4. Inspect the generated diff
Before accepting generated code, confirm that each test calls the intended code and asserts a meaningful result. Expected values should come from the contract, not from calling the function under test to calculate the answer. Check that the tests do not silently change implementation code when the goal is to expose existing behavior.
5. Run the narrow test, then expand
Use the project’s established command to run the new test or the smallest relevant suite first; then run the related suite. Review failures and skipped tests. A failure may indicate a setup issue, an incorrect test expectation, or a defect in the implementation, so diagnose it before changing code or weakening an assertion.
How to generate QA cases from requirements
Start with the requirement and check what the tool imports
Use a specific requirement or work item as the source of truth. Import behavior can be limited: Katalon’s AI test-generation documentation says its described Jira and Azure DevOps workflow retrieves the summary and description, supports image attachments for AI interpretation, and does not support other attachment formats in that workflow.
Clarify gaps and review the generated cases
When a requirement is ambiguous, add context or resolve the ambiguity before approving cases. Check preconditions, steps, and links to the originating requirement; then edit, save, or discard the output. Katalon states: “Always review the generated test case content before approving it. AI-generated results may contain errors.”
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Keep QA artifacts distinct from automated test code
A requirement-derived case is not automatically an executable unit or integration test. Connect it to automation only if the team’s tools and process support that link, and verify the resulting automation separately.
What to include in a generation prompt
A useful request provides enough context to keep the output inside the project’s real boundaries. Adapt this outline to the tool and language:
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- Target: the specific function, class, module, or requirement.
- Expected behavior: the relevant contract, including defined boundaries and errors.
- Project context: framework, runner, test location, naming, and a representative neighboring test.
- Conventions: fixtures, setup, mocking, and any project-specific instructions.
- Scope: the cases wanted and whether the tool should create a separate file or edit an existing one.
- Constraints: ask it not to alter implementation code when generating tests to check current behavior.
In Visual Studio Code, Microsoft’s examples include prompts such as “Generate tests for this code” and “Generate tests for this code #file:calculator.js.” A focused target and nearby examples give the tool useful context; the generated assertions still need review.
Best Value
How to assess a test-generation feature
Compare a tool’s fit against the workflow you already use, rather than assuming a feature label guarantees quality.
- Language and test types: verify support for your language, framework, and unit, integration, or end-to-end needs.
- Context: determine whether it can use selected source, neighboring tests, repository conventions, or linked requirements.
- Output placement: check whether it creates a separate file, edits an existing test, or produces manual cases and steps.
- Review controls: see how you inspect, edit, accept, or discard suggestions.
- Execution workflow: confirm that test discovery, running, debugging, and result inspection fit the team’s tools.
- Operational terms: verify current licensing, data handling, integrations, and availability before adopting a feature.
Android Studio’s documentation describes AI-assisted Java and Kotlin unit-test generation using project configuration, while noting that performance or compatibility can vary by model. GitHub and Microsoft likewise provide workflow guidance, but the cited documentation does not establish that one product produces more accurate or complete tests than another. Check current vendor documentation for changing interfaces and availability.
Common mistakes to avoid
- Testing implementation rather than intended behavior: this can turn a bug into an expected result. Base assertions on requirements or documented contracts.
- Generating too broad a suite at once: start with a small, reviewable target so mismatched assumptions are easier to find.
- Accepting plausible-looking assertions without checking them: verify inputs, expected values, error behavior, and the code under test.
- Adding a new framework unnecessarily: use the project’s existing runner and conventions.
- Treating generated cases as executed tests: generation and review do not replace running the suite and inspecting its results.
- Assuming more cases always means better coverage: prioritize cases that trace to defined behavior, not an arbitrary count.
What test generation can and cannot establish
Official documentation supports test-generation workflows and explicitly cautions that output may miss scenarios or contain errors. It does not provide a neutral, directly comparable benchmark establishing a general improvement in test quality, time saved, defect reduction, or coverage. Treat generated output as a draft: its value depends on the contract and context supplied, the quality of review, and the results when the tests run.
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