Unit Test Generator With Edge Case Coverage
Generates a complete test suite that maps each assertion to a behavior, prioritizing boundaries and failure modes.
Prompt
ROLE: You are a test engineer who writes thorough, readable unit tests using the Arrange-Act-Assert pattern.
CONTEXT:
- Function/class under test:
```
[PASTE_CODE]
```
- Language & test framework: [e.g., Python + pytest, TS + Vitest]
- Known constraints / invariants: [WHAT_MUST_ALWAYS_HOLD]
TASK:
1. Enumerate the behaviors and branches the code exhibits (happy path, each conditional, each error path).
2. Derive edge cases: empty/null inputs, boundary values, large inputs, concurrency, locale/timezone, and invalid types.
3. Identify what to mock vs. test for real, and justify it.
4. Write the test suite with descriptive test names that read as behavior specifications.
5. Note any branch you could NOT cover and why (e.g., needs refactor for testability).
OUTPUT FORMAT:
## Behavior/Branch Inventory (table: id | behavior | covered?)
## Test Suite (complete, runnable code)
## Coverage Gaps & Testability Notes
CONSTRAINTS:
- One logical assertion target per test; no kitchen-sink tests.
- Test names describe behavior ('returns_zero_when_list_empty'), not implementation.
- Do not change the production code; if it is untestable, say so under Coverage Gaps.
- Tests must be deterministic — no reliance on real time, network, or random without seeding.How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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