---
name: property-based-testing-f0e2aa98f376
description: >
  Design property-based tests that verify code properties hold for all inputs
  using automatic test case generation. Use for property-based, QuickCheck,
  hypothesis testing, generative testing, and invariant verification.
---

# Property-Based Testing

## Table of Contents

- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)

## Overview

Property-based testing verifies that code satisfies general properties or invariants for a wide range of automatically generated inputs, rather than testing specific examples. This approach finds edge cases and bugs that example-based tests often miss.

## When to Use

- Testing algorithms with mathematical properties
- Verifying invariants that should always hold
- Finding edge cases automatically
- Testing parsers and serializers (round-trip properties)
- Validating data transformations
- Testing sorting, searching, and data structure operations
- Discovering unexpected input combinations

## Quick Start

Minimal working example:

```python
# test_string_operations.py
import pytest
from hypothesis import given, strategies as st, assume, example

def reverse_string(s: str) -> str:
    """Reverse a string."""
    return s[::-1]

class TestStringOperations:
    @given(st.text())
    def test_reverse_twice_returns_original(self, s):
        """Property: Reversing twice returns the original string."""
        assert reverse_string(reverse_string(s)) == s

    @given(st.text())
    def test_reverse_length_unchanged(self, s):
        """Property: Reverse doesn't change length."""
        assert len(reverse_string(s)) == len(s)

    @given(st.text(min_size=1))
    def test_reverse_first_becomes_last(self, s):
        """Property: First char becomes last after reverse."""
        reversed_s = reverse_string(s)
        assert s[0] == reversed_s[-1]
        assert s[-1] == reversed_s[0]
// ... (see reference guides for full implementation)
```

## Reference Guides

Detailed implementations in the `references/` directory:

| Guide | Contents |
|---|---|
| [Hypothesis for Python](references/hypothesis-for-python.md) | Hypothesis for Python |
| [fast-check for JavaScript/TypeScript](references/fast-check-for-javascripttypescript.md) | fast-check for JavaScript/TypeScript |
| [junit-quickcheck for Java](references/junit-quickcheck-for-java.md) | junit-quickcheck for Java |

## Best Practices

### ✅ DO

- Focus on general properties, not specific cases
- Test mathematical properties (commutativity, associativity)
- Verify round-trip encoding/decoding
- Use shrinking to find minimal failing cases
- Combine with example-based tests for known edge cases
- Test invariants that should always hold
- Generate realistic input distributions

### ❌ DON'T

- Test properties that are tautologies
- Over-constrain input generation
- Ignore shrunk test failures
- Replace all example tests with properties
- Test implementation details
- Generate invalid inputs without constraints
- Forget to handle edge cases in generators


---
Source attribution: aj-geddes
Original project: https://github.com/aj-geddes/useful-ai-prompts/blob/3f5182cfd739fc113f4af5244a1cf342ad7f7911/skills/property-based-testing/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 AJ Geddes

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


# Mega AI Hub resource metadata

Creator / publisher: aj-geddes
Original project: https://github.com/aj-geddes/useful-ai-prompts/blob/3f5182cfd739fc113f4af5244a1cf342ad7f7911/skills/property-based-testing/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Scientist, QA / Test Engineer
Specialties: Cross Platform, Testing
Tools: Hypothesis, Pytest
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: dc36f16487ac76d29d69412198a11166297d16cd2278d475c4698ada57bdb613