AI Test Case Generator: Examples and Free CSV Template
Draft test cases from requirements with an original QA matrix, free CSV example and editable Brainbaby brief. Keep execution results separate from drafts.
An AI test case generator helps turn requirements into a draft of the checks a person or test runner should perform. A useful result connects each expected outcome to an approved rule, explains the setup and leaves the actual result empty. A long list of scenarios alone cannot tell you whether the software works.
This guide shows a small requirement-to-case matrix, eight fictional manual cases and a review prompt you can adapt in Brainbaby. Download the blank test case worksheet and the worked CSV example without an account. To draft cases from your own requirements, prepare your test case brief. The planner organizes text locally; AI drafting is a separate chat action using your account allowance.
What should an AI test case generator produce?
Start with a case another tester can follow without guessing. The minimum useful fields are a stable case ID, requirement reference, preconditions, test data, numbered actions, expected result and execution status. Add priority with a reason, then reserve separate fields for the actual result, evidence, tester, build and run date.
Keep the distinction between a test scenario and a test case visible. “Check topic validation” is a scenario. “Enter 81 ASCII characters into the topic field, submit once and check that the form rejects it without adding an item” is a case, provided an approved rule defines that limit. An executable script is a further deliverable with environment setup and assertions. A written case does not become an executed test because AI drafted it.
If your input is still an app idea, use the AI PRD generator guide first. It helps separate proposed behavior from approved acceptance criteria. Do not let a drafting tool silently promote an assumption into a pass condition.
Worked example: a workshop request list
The following requirements are approved only inside this fictional teaching example. They are not Brainbaby features, customer records or evidence of tested software. The imaginary prototype uses local demonstration data.
| Source | Approved behavior in the tutorial |
|---|---|
| R-1 | Trim the topic; reject an empty value after trimming. |
| R-2 | Accept a topic containing 1–80 ASCII characters after trimming; reject 81. |
| R-3 | A valid accepted submission adds one pending request. |
| R-4 | The Pending filter displays pending requests only. |
| R-5 | An empty list shows “No requests yet.” |
Repeated-submit handling and storage-failure messaging are undecided. Non-ASCII character counting and production persistence are also outside these approved rules. They need decisions before exact expectations can be written. This is deliberately small: a team can inspect each rule, rather than approve a large speculative matrix.
Manual regression matrix with eight cases
| Case | Source | Setup and action | Expected result | Initial status |
|---|---|---|---|---|
| TC-1 | R-1, R-2, R-3 | Use an empty local list. Enter “ Woodworking ” and submit once. | One pending item named “Woodworking” appears. | Not run |
| TC-2 | R-1 | Enter three ordinary spaces and submit once. | Validation rejects the value; no item is added. | Not run |
| TC-3 | R-2, R-3 | Enter exactly 80 ASCII “A” characters and submit once. | One pending item with that topic appears. | Not run |
| TC-4 | R-2 | Enter exactly 81 ASCII “A” characters and submit once. | Validation rejects the topic; no item is added. | Not run |
| TC-5 | R-5 | Open a list with no requests. | “No requests yet” is visible. | Not run |
| TC-6 | R-4 | Seed one pending and one reviewed item. Select Pending. | Only the pending item is visible. | Not run |
| TC-7 | Undecided | Consider two rapid submissions of the same valid form. | TO CONFIRM: duplicate-prevention behavior needs approval. | Blocked — requirement undecided |
| TC-8 | Undecided | Consider a local storage write failure. | TO CONFIRM: failure message and retained input need approval. | Blocked — requirement undecided |
The CSV has these same eight cases and separate blank execution fields. There are six draft cases with defined expectations and two blocked questions. That count is not a coverage percentage or a pass rate. A matrix can reveal which written requirement lacks a case; it cannot establish that every important product risk has been considered.
How to generate test cases from requirements in Brainbaby
1. Choose a narrow source excerpt
Paste approved requirements and their IDs into the planner below. Keep the source under its 5,000-character limit. Choose a coherent workflow, such as creating and filtering a request, rather than silently truncating a whole product specification. Split a larger document into named sections and retain the source version for later review.
Remove credentials, personal records and confidential material you are not authorized to share. A screenshot may explain an interface, but it cannot prove an unseen server rule or describe every account role. State which context you actually supplied.
2. Specify coverage and unresolved decisions
Ask for a normal path, invalid inputs, boundaries and state changes relevant to the supplied rules. Specify the platform and reviewer. When a behavior is undecided, ask for a question with a suggested owner. Invented expectations can cause a tester to reject correct software or approve the wrong behavior.
For the workshop example, the 80-character boundary is explicit and can produce two precise cases. A recovery message after storage failure is unspecified and must stay blocked. A reviewer can approve that rule later, then update the case and its source reference together.
3. Review the brief, then send it yourself
The local planner produces an editable request for AI drafting, not a generated answer. You can copy it or continue to an unsent chat draft in the same browser tab, including through sign-in. Source text stays out of that navigation link and interaction analytics. Continuing does not send a message, execute a test or start an app build.
When you send the reviewed draft in chat, the request uses your account allowance. Check current plans and allowances before choosing a plan. There is no unlimited testing promise or separate free AI execution allowance in this guide.
4. Check the AI draft against the source
Read each expected result alongside its requirement. Remove duplicate cases and unsupported assumptions. Confirm that preconditions are achievable in a controlled environment. Replace broad steps such as “test the form” with actions a colleague can repeat. Leave actual results empty and initial status as Not run, or blocked when an expectation is undecided.
If you request CSV text from chat, inspect it before opening or importing it. Quote cells containing commas, quotes or line breaks. Treat formula-like values beginning with characters such as equals as untrusted spreadsheet input. The downloadable example is a static teaching file; it is not a universal importer for your tracker.
Brainbaby, test-management tools and Playwright
Choose the workflow based on the work you need after the draft.
| Need | Starting point | What still requires verification |
|---|---|---|
| Discuss unclear requirements and draft a small matrix | Brainbaby chat with the reviewed brief | Review the answer, save approved cases and execute them separately. |
| Maintain a test repository, requirement links and run records | A dedicated test-management product | Check its documented features, permissions and fit for your team. |
| Execute repeatable browser checks in your development workflow | A test runner such as Playwright | Implement, run and debug the scripts against an owned environment. |
TestCaseLab documents AI-assisted requirement and test-case drafts that users review before saving in its projects. Brainbaby's workflow here does not claim the same test repository, run tracking or synchronization. Playwright's best practices emphasize user-visible behavior and isolated tests. Use those principles when turning an approved case into automation; a chat answer alone does not establish a passing browser run.
From a failed case to a useful bug report
Run approved cases in your own controlled environment. Record the exact build, setup, actual outcome and evidence for that run. Keep a blocked case separate from a failed case: a missing requirement is a decision gap, not proof of a software defect.
When an observed result differs from an approved expectation, use the AI bug report generator guide. It carries the source rule and case ID into a report while keeping an unverified root-cause theory out of the observed result. For help reviewing the related implementation, start with Brainbaby's coding workflow and provide only the repository access or excerpts you intend to share.
Questions before you rely on the result
Can I generate test cases from user stories?
Yes, you can request a draft from a user story and its acceptance criteria. Supply the actual criteria and mark missing rules. A story title alone rarely defines enough behavior for precise boundaries and failure cases.
Does Brainbaby run these cases or create Jira tickets?
This guide's planner prepares a writing brief and opens an editable chat draft. It does not run tests, create tickets or synchronize cases. Copy reviewed cases into the tools your team uses and record execution there.
Can I use the example in Excel or Google Sheets?
You can download the CSV and inspect its eight fictional rows in a spreadsheet. Check delimiter and text handling on import. Actual results, evidence and run dates remain blank until you perform a real run.
Is the worksheet free?
The worksheet, CSV example and local brief preparation are free without an account. AI drafting is a separate action in Brainbaby chat under your current allowance. Prepare your brief with one approved requirement first, then judge the draft against that source before expanding the scope.
Turn approved requirements into reviewable test cases.
Download a blank worksheet and the fictional CSV example, or prepare a brief below. Keep expected behavior linked to its source and actual results empty until someone runs the case.
Prepare my test case briefDownloads and local brief preparation are free without an account. AI drafting starts only when you send the editable chat draft and uses your account allowance. This flow does not execute tests, measure coverage or sync a test-management tool.
Review chat plans and allowancesPrepare your test case brief
Organize the task here, then open it as an editable chat draft. Preparing a brief runs locally. It does not generate an AI answer, spend credits or send anything.
Your source text stays out of the link and interaction analytics. Continuing preserves the draft in this browser tab for sign-in. You choose when to send it; the AI request uses your account allowance. View current plans.