Pin down behaviour and dependencies
The unit under test, its expected behaviour, and exactly what it depends on are captured separately, so the generated tests mock only what's real and assert only what you actually specified.
TEST GENERATION PROMPT
Unit Test Prompt Builder
Specify the behaviour, dependencies, cases, and conventions an AI coding tool needs to generate focused unit tests, not a generic scaffold.
- Uses your inputs in a transparent calculation
- Instant result with practical next steps
- No signup required
Planning guidance only. Validate important decisions with customer evidence and your delivery team.
YOUR INPUTS
Build your result
Complete every field. The result updates only when you choose Calculate.
How it works
List cases explicitly
Each case you list becomes one numbered test requirement in the assembled prompt. The tool also classifies each as positive or negative/edge, so you can see at a glance whether coverage is balanced.
Score completeness before you send it
Behaviour detail, dependency coverage, case count, positive/negative balance, and whether project conventions were supplied are rolled into a single completeness score, with concrete gaps called out.
Continue learning: How AI coding tools invent fake packages · How to evaluate AI coding tools for your team
Frequently asked questions
Why does the tool separate positive and negative cases?
A prompt with only happy-path cases produces tests that pass trivially and miss real bugs. The scan flags when no invalid-input, error, or edge case is listed so you can add one before generating.
Do I need to paste the actual source code?
No — this tool builds the prompt text, not the test code. Paste the assembled prompt into your AI coding tool alongside the real source file so it has both the specification and the implementation to work from.
What if my unit has no external dependencies?
Leave the dependencies field empty or write "none" — the prompt will state that explicitly so the model doesn't invent mocks for calls that don't exist.
Why does the prompt tell the model not to invent untested requirements?
AI coding tools sometimes add extra assertions or tests for behaviour you never described, which can lock in incidental implementation details as if they were requirements. The instruction keeps generated tests scoped to what you actually specified.
How is this different from a test-case generator?
A test-case generator produces a list of scenarios to cover. This tool takes that list, plus behaviour, dependencies, and conventions, and assembles it into one complete prompt ready to hand to an AI coding tool for actual test code generation.
How We Compare
| Feature | MVPHub | GitHub Copilot | Cursor |
|---|---|---|---|
| Focused input-based assessment | Included | Limited | Limited |
| Transparent calculation | Included | Limited | Limited |
| No repository access required | Included | Limited | Limited |
| Workflow-specific next steps | Included | Limited | Limited |
GitHub Copilot and Cursor can generate tests directly from an open file inside the editor. MVPHub instead assembles a complete, portable prompt from your structured inputs, scored for completeness, that you can paste into either tool or any other AI assistant.
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