Code

Generate unit tests from your code

Paste your function and get a ready-to-run test suite covering the important cases.

For the best results: Paste your full code or describe the goal clearly. Mention the language, what the output should do, and any constraints or libraries required.

AI can make mistakes. Check important information before relying on it.

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What is Unit Test Generator?

Unit Test Generator is a free AI coding assistant that takes a function and returns a ready-to-run test suite covering the cases that matter. Each request is handed to a code-specialized model chosen for the task, returning working code, bug fixes, and explanations in any programming language in seconds. You paste and go, with no login and nothing to install, and the code you share is not retained once the session ends.

✦Paste code and go, no login, nothing to install
✦Code-specialized models are selected per request automatically
✦Your code is not retained after the session ends

Models: It routes across strong coding models including Llama, Gemini and Mistral families, picking the best available one per request. The exact model used is shown after every response.

See it in action

A developer inherited an untested pricing module and wants a safety net around a discount function before refactoring it.

What you type

Generate pytest tests for this: def apply_discount(price, percent): if percent < 0 or percent > 100: raise ValueError("percent must be between 0 and 100") return round(price * (1 - percent / 100), 2)

What you get

import pytest from pricing import apply_discount def test_applies_percentage(): assert apply_discount(100.0, 20) == 80.0 def test_zero_percent_leaves_price_unchanged(): assert apply_discount(59.99, 0) == 59.99 def test_full_discount_is_free(): assert apply_discount(100.0, 100) == 0.0 def test_rounds_to_two_decimals(): # 9.99 * 0.85 = 8.4915 -> 8.49 assert apply_discount(9.99, 15) == 8.49 @pytest.mark.parametrize("bad_percent", [-1, 101, 150]) def test_out_of_range_percent_raises(bad_percent): with pytest.raises(ValueError): apply_discount(100.0, bad_percent)

Tip: The generator finds the structural edges on its own: the 0 and 100 boundaries, the rounding case, and the out-of-range values that should raise. What it cannot see are your domain rules. Should a 100% discount even be allowed, and can price arrive negative? Name the rules you actually care about in a sentence next to the code and you get a test per rule, instead of a tidy suite that quietly locks in behavior you never intended.

Use cases

What people use it for

Put a safety net of tests under inherited legacy code before you dare refactor it.

Catch the edge cases you didn't think of in a date or currency handling function.

Close the coverage gap flagged in code review without losing an afternoon to boilerplate.

Who uses Unit Test Generator?

TDD-curious developers

generating a first test suite for an untested function so they can start refactoring with a safety net in place

Code reviewers

producing tests for a colleague pull request to check the edge cases the author might have skipped

Legacy-code maintainers

pinning down what an old function currently does with characterization tests before they dare change it

How it works

1Paste your code or describe what you need built.
2A code-specialized model writes, fixes, or explains it.
3Copy the code out, or ask follow-ups to iterate on it.

Tips for the best results

Name your test framework and assertion style, so the suite drops into your project without you rewriting the syntax.
Describe what the function is supposed to do, especially the rules that are not obvious from the code, so the assertions test intent rather than just echoing current behavior.
Point out the tricky inputs, such as empty values, negatives, huge numbers or unexpected types, so the generated edge cases cover the failures you actually worry about.
Flag any external calls the function makes and say whether to mock them, otherwise the tests may try to hit a real service and fail for the wrong reason.
When a generated test fails, check whether the test is wrong or the code is; a failing test that reveals a real bug is the tool doing its job.

FAQ

Frequently asked questions

Which testing framework do the tests come in?

It's inferred from your language, pytest for Python, Jest for JavaScript, JUnit for Java and so on. If your project uses something else, or a particular assertion style, say so when you paste the function. Mentioning quirks of your setup, like custom fixtures or unusual file naming, saves a round of fixing imports later.

How does it know what my function is supposed to do?

It reads the signature, names, types and logic, which means a buggy function can receive tests that faithfully enshrine the bug. If the behavior is at all subtle, state the intent in a sentence alongside the code. When your description and the code disagree, the generated tests expose the mismatch, which is often the bug found before anything runs.

Will I get real edge cases or just the happy path?

Boundaries, empty inputs, nulls and error paths are covered by design, that's the point of the tool. What it can't know are your domain's edge cases: the leap-year rule that matters to your billing, the legacy ID format some customers still use. Mention those and ask for a test on each; domain knowledge is the one input only you hold.

My function talks to a database. Can it still be tested?

Describe the dependency and you'll get mocks or stubs in your framework's idiom, with the tests wired to use them. Review the mocks closely, though. They encode assumptions about how the real dependency behaves, and a wrong assumption gives you tests that pass while production fails. Where behavior is uncertain, check it against the dependency's actual documentation.

Some of the generated tests fail when I run them. Is that bad?

Sometimes it's the whole value, a failing test can be surfacing a genuine bug in the function. Otherwise the cause is usually mundane: imports, paths or fixture setup that doesn't match your project, all quick to fix. Paste the failure output back and it will repair the suite. The one rule: never delete a failing test before knowing which kind it is.

Is this unit test generator free?

Yes. Paste a function and get a runnable test suite for free, with no account, sign-up or payment and no cap behind a login. It writes the tests from the code and any behavior you describe. You run them in your own project, so review the assertions before relying on a green result.

Can it mock a database, API or other dependency?

Yes. Tell it which parts are external, whether a database call, an HTTP request or the clock, and it writes tests that mock or stub them so the suite runs fast and deterministically without real infrastructure. Name your mocking library if you have a preference, otherwise it picks the common one for your framework.

Which languages and test frameworks does it support?

The mainstream ones: Jest, Vitest and Mocha for JavaScript and TypeScript, pytest and unittest for Python, JUnit for Java, RSpec for Ruby, the Go testing package, PHPUnit and more. Name the framework you use so the syntax, assertions and file layout match your project; otherwise it defaults to the popular choice for that language.

What are the usage limits?

Code tools are text tools, so they are unmetered during the current free launch phase, iterate on a bug or generate as many drafts as the work needs. A short per-minute limit guards against scripted abuse only. Paid plans with higher limits and priority model access are coming soon.

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