property-based-testing

name	property-based-testing
description	Hypothesis property-based testing strategies for Python with custom strategies for financial data, API responses, and stateful testing
triggers	["property based testing","hypothesis","fuzzing","mutation testing","mutmut","generative testing"]

Property-Based Testing

Use Hypothesis to find edge cases automatically. Includes custom strategies for financial data, API responses, and stateful testing.

Core Patterns

from hypothesis import given, strategies as st, settings, assume
import hypothesis.extra.numpy as npst

# Basic property test
@given(st.lists(st.integers()))
def test_sort_is_idempotent(xs):
    assert sorted(sorted(xs)) == sorted(xs)

@given(st.text(min_size=1))
def test_string_roundtrip(s):
    encoded = s.encode("utf-8")
    assert encoded.decode("utf-8") == s

Custom Strategies for Financial Data

from hypothesis import strategies as st
from decimal import Decimal

# Price strategy (realistic trading range)
prices = st.decimals(min_value=Decimal("0.01"), max_value=Decimal("1000000"),
                     places=8, allow_nan=False, allow_infinity=False)

# Order quantity
quantities = st.decimals(min_value=Decimal("0.001"), max_value=Decimal("10000"),
                         places=3, allow_nan=False, allow_infinity=False)

# OHLCV candle
@st.composite
def candles(draw):
    open_price = draw(prices)
    close_price = draw(prices)
    high = max(open_price, close_price) + draw(st.decimals(
        min_value=Decimal("0"), max_value=Decimal("100"), places=8))
    low = min(open_price, close_price) - draw(st.decimals(
        min_value=Decimal("0"), max_value=min(open_price, close_price), places=8))
    volume = draw(quantities)
    return {"open": open_price, "high": high, "low": low, "close": close_price, "volume": volume}

@given(candle=candles())
def test_candle_invariants(candle):
    assert candle["high"] >= candle["low"]
     candle[] >= candle[]
     candle[] >= candle[]
     candle[] <= candle[]
     candle[] <= candle[]

Stateful Testing

from hypothesis.stateful import RuleBasedStateMachine, rule, invariant, initialize

class AgentPoolMachine(RuleBasedStateMachine):
    def __init__(self):
        super().__init__()
        self.pool = AgentPool(max_size=5)
        self.expected_count = 0

    @rule(name=st.text(min_size=1, max_size=50))
    def add_agent(self, name):
        if self.expected_count < 5:
            self.pool.add(Agent(name=name))
            self.expected_count += 1

    @rule()
    def remove_random(self):
        if self.expected_count > 0:
            self.pool.remove_oldest()
            self.expected_count -= 1

    @invariant()
    def count_matches(self):
        assert len(self.pool) == self.expected_count

    @invariant()
    def never_exceeds_max():
         (.pool) <= 

TestAgentPool = AgentPoolMachine.TestCase

Mutation Testing with mutmut

# Run mutation testing
mutmut run --paths-to-mutate=src/jarvis/core/ --tests-dir=tests/unit/

# View surviving mutants
mutmut results

# Show specific mutant
mutmut show 42

# Apply mutant to see what changed
mutmut apply 42

Surviving mutants = code that can be changed without test failure = missing test coverage.

pytest Integration

# pyproject.toml
[tool.hypothesis]
max_examples = 200
deadline = 5000  # ms
database_backend = "directory"
suppress_health_check = ["too_slow"]

[tool.pytest.ini_options]
markers = [
    "hypothesis: property-based tests",
]

# Run with statistics
pytest -v -k "hypothesis" --hypothesis-show-statistics

# Run with specific seed (reproduce failure)
pytest --hypothesis-seed=12345