The Tool Desk
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Start with a test basis, not a vague request
AI can draft tests only as well as it understands what the software is supposed to do. Provide one or more concrete sources of expected behavior:
- The function or module under test, for tests around existing code.
- A user story, acceptance criteria, API contract, or specification, for tests derived from requirements.
- Examples of valid inputs and expected outputs, especially when behavior is subtle.
- An adjacent test file or a short description of the framework and local conventions.
Say which language and test framework to use. If the requirements are unclear, ask the model to list ambiguities and questions before writing tests. Do not let it turn an undocumented assumption into an expected result. The ISTQB CT-GenAI syllabus describes using generative AI to analyze test-basis material, identify ambiguities, and propose clarification questions.
Ask for a focused spread of scenarios
Request cases that exercise distinct behavior rather than a large pile of similar examples. Depending on the code, useful categories include:
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- Normal behavior: representative valid inputs and expected results.
- Boundaries: values just below, at, and just above limits; empty collections; minimum or maximum lengths.
- Invalid inputs and states: malformed values, unsupported options, missing required data, or invalid transitions.
- Exceptions and failures: expected errors, dependency failures, timeouts, or recovery behavior where relevant.
- Important branches: conditions, fallback paths, permissions, and other decision points that could change the outcome.
Not every category applies to every function. Ask the AI to explain which requirement each proposed test checks and to flag categories it could not meaningfully cover. GitHub’s guidance recommends detailed scenario prompts and specifically calls out edge cases, exception handling, and data validation: Writing tests with GitHub Copilot.
Use a prompt that makes expectations and limits explicit
Adapt this template to the codebase. Replace the bracketed directions with your real requirements and conventions; the wording is an example, not a universally effective prompt.
Using the requirements and code below, propose a focused set of tests in [language] with [test framework]. Follow the conventions in the existing test example. Cover normal behavior, relevant boundaries, invalid inputs, exceptions, and important branches. For each test, state the requirement it verifies and the expected result. List ambiguities or assumptions before writing code; do not invent undocumented business rules. Keep tests focused, use meaningful assertions, and explain any mock or fixture assumptions.
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For a codebase with an existing suite, a useful follow-up is: “Compare these proposed cases with the existing tests. Identify important gaps or duplicates. Do not change files until the cases are reviewed.” Providing nearby tests gives the model a better chance of matching the project’s naming, setup, and assertion style.
Review the proposal before adding it
Treat generated tests as suggestions, not verified specifications. Before accepting each case, check:
- Requirement fidelity: Does the assertion test behavior the requirements actually promise?
- Expected results: Is the expected value supported by a requirement or example, rather than guessed from the implementation?
- Test purpose: Does the test check externally meaningful behavior, or merely repeat an implementation detail that may change safely?
- Setup and doubles: Do fixtures and mocks represent realistic conditions? Are mocks isolating a dependency for a reason, rather than hiding behavior the test should exercise?
- Coverage of cases: Are important branches and failure paths represented, without redundant tests that assert the same thing?
- Maintainability: Are names descriptive, assertions meaningful, and setup no more complicated than needed?
Ask the AI to list assumptions and missing cases if that makes review easier, but check its answers yourself. GitHub cautions that generated tests can miss cases and should be reviewed: GitHub’s testing guidance.
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Run the tests and interpret failures
Add only reviewed cases, then run them with the project’s normal test command and environment. A passing test suite establishes that the assertions passed in that run; it does not establish that the tests captured every requirement or that their expectations are correct.
- Run the new tests using the repository’s existing test runner and configuration.
- If a test fails to parse or execute, inspect generated syntax, imports, fixtures, mock setup, and framework APIs.
- If an assertion fails, compare the actual result with the requirement. The test may expose a product bug, or its expectation may be wrong.
- Correct the cause, rerun the relevant tests, and then run the broader suite to check for regressions.
- Review the final tests in context with existing tests before merging or relying on them.
Microsoft’s VS Code guide likewise describes comparing proposed tests with existing ones, adding agreed cases, running them, and investigating failures: Test existing code with AI.
Choose the right AI-assisted approach
| Approach | Useful when | Watch for |
|---|---|---|
| Code-context prompting | You have an existing function or module and want tests in the project’s framework and style. | Code alone may not reveal intended behavior; provide requirements and expected examples too. |
| Requirement or specification prompting | You are deriving scenarios, expected results, or test data before or alongside implementation. | Surface ambiguous rules as questions instead of accepting invented expectations. |
| Property-based testing | You can state a general invariant and want to explore many generated inputs for counterexamples. | It complements selected example cases; the property and any discovered failures still need human review. |
The Anthropic account of AI-assisted property-based testing describes an agent writing such tests. Consider this approach when a broad input space can be checked against a clear invariant, not as a replacement for carefully chosen examples.
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Protect code and data, and avoid misleading measures
Generated tests may be invalid, misunderstand intended behavior, or encode an incorrect expectation. A high test count or line-coverage figure alone does not show that assertions are meaningful or that important behavior is protected. The practical safeguards are clear requirements, review, execution, and comparison with existing tests.
Also follow your organization’s rules before sending source code, test data, or confidential requirements to an external AI service. The current ISTQB CT-GenAI certification page, checked on October 3, 2026, lists syllabus version 1.1 and covers prompt engineering, evaluation of GenAI results, hallucinations, bias, privacy, security, and AI-assisted testing approaches. It also lists CTFL as a prerequisite; check the official CT-GenAI page for current certification and provider details.
Or skip the browser setup
If part of your testing workflow needs screenshots of pages—for example, visual checks of a test environment—you can request one from ScreenshotNeo, a website screenshot API and MCP server. This is separate from generating test cases; it does not validate software requirements or replace your test runner.
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One GET request returns an image or PDF. The API supports PNG, JPEG, and WebP output. See the ScreenshotNeo API documentation.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpScreenshotNeo accepts cookie and consent banners and removes more than 60 known consent platforms, newsletter popups, and chat widgets before capture; each of those steps can be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and the response reports the page verdict and billing status in headers. Its MCP server provides take_screenshot, get_page_info, and capture_pdf tools for Claude, Cursor, and other MCP clients. The Free plan includes 1,000 shots a month with no card; paid plans start at $5 for 3,000 shots.
Sign up for 1,000 free screenshots a month—no card required.
Frequently Asked Questions
Can AI-generated tests tell me whether my software is correct?
No. They are candidate checks whose value depends on accurate requirements, meaningful assertions, and review; passing tests do not prove correctness.
Should I give an AI assistant my whole repository?
Only if that is allowed by your organization’s data-handling and security rules. Share the minimum relevant code and test context permitted by policy.
What should I do when the requirements do not specify an expected result?
Ask for the ambiguity and the questions that need answering. Do not accept a guessed expected result as a requirement.
Quick Recap
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