Property Based Testing

Property-Based Testing — Test Once, Verify Against Thousands

Core Concept

Instead of writing specific test cases, you write properties — statements about what your code MUST do for ALL inputs:

Unit test:  "sort([3,1,2]) == [1,2,3]"         → 1 case
Property:   "sorted(xs) == sorted(sorted(xs))"  → thousands of cases

The testing library generates random inputs, runs the property, and shrinks failures to minimal counterexamples.

The Three Phases of PBT

1. GENERATE  → library produces random inputs from domain
2. RUN       → property function evaluated for each input
3. SHRINK    → on failure, reduce input to smallest counterexample

Shrinking — Why It Matters

Shrink failure: [3, 9, 7, 1, 8, 2, 5] → [] (empty list triggers the bug) Shrinking transforms massive inputs into minimal, readable bugs.

When PBT Beats Unit Testing

Language Tooling

Python + Hypothesis

Setup

pip install hypothesis pytest

Basic Pattern

from hypothesis import given, strategies as st, assume, settings

@given(st.lists(st.integers(min_value=1, max_value=1000), min_size=1))
def test_sort_preserves_elements(xs):
    """Sorting never loses or duplicates elements."""
    sorted_xs = sorted(xs)
    assert len(sorted_xs) == len(xs)
    assert set(sorted_xs) == set(xs)          # preservation
    assert sorted_xs == sorted(sorted_xs)      # idempotence

@given(st.lists(st.integers()), st.lists(st.integers()))
def test_merge_preserves_all(a, b):
    """Merging two lists preserves all elements."""
    merged = sorted(a + b)
    expected = sorted(sorted(a) + sorted(b))
    assert merged == expected

Composite Strategies (Realistic Data)

# Realistic API-like data
@st.composite
def api_response(draw):
    status = draw(st.sampled_from([200, 201, 400, 404, 500]))
    body = draw(st.dictionaries(
        st.text(min_size=1),
        st.one_of(st.none(), st.text(), st.integers())
    ))
    return {"status": status, "body": body, "timestamp": draw(st.floats(min_value=0))}

@given(api_response())
def test_api_response_roundtrip(response):
    """API response JSON serialize/deserialize preserves structure."""
    import json
    serialized = json.dumps(response)
    deserialized = json.loads(serialized)
    assert deserialized == response

Settings for Real Projects

from hypothesis import Phase, settings

@settings(
    max_examples=500,          # more examples = higher confidence
    deadline=2000,             # 2s per example (fail fast on slow code)
    phases=[Phase.generate, Phase.shrink],  # shrink failures
    database=ExampleDatabase(".hypothesis/examples"),  # save known failures
    print_blob=True,           # print counterexamples
)
@given(st.integers())
def test_divide_by_zero_safe(x):
    result = safe_divide(10, x)
    if x == 0:
        assert result is None or result == float('inf')
    else:
        assert result == 10 / x

Stateful Testing (Advanced)

from hypothesis.stateful import Rule, rule, invariant

class StackModel:
    def __init__(self):
        self.items = []

    def push(self, x):
        self.items.append(x)

    def pop(self):
        if self.items:
            return self.items.pop()
        raise IndexError("pop from empty")

class Stack harness(StatedFile:
    def __init__(self):
        self.target = StackModel()

    @rule(x=st.integers())
    def push(self, x):
        self.target.push(x)
        assert len(self.target.items) == len(self.items) + 1
        self.items.append(x)

    @rule()
    def pop(self):
        if self.target.items:
            result = self.target.pop()
            assert result == self.items.pop()
        else:
            with selfRaises(IndexError):
                self.target.pop()

TestStack.harness().run_test(n=500)

Go + testing/quick

Basic Pattern

func TestReversePreservesLength(t *testing.T) {
    f := func(xs []int) bool {
        reversed := reverse(reverse(xs))
        return len(reversed) == len(xs)
    }
    if err := quick.Check(f, nil); err != nil {
        t.Error(err)
    }
}

func reverse(xs []int) []int {
    result := make([]int, len(xs))
    copy(result, xs)
    for i, j := 0, len(result)-1; i < j; i, j = i+1, j-1 {
        result[i], result[j] = result[j], result[i]
    }
    return result
}

With Custom Generator

// Generate non-empty slices of ints
func intSlice(ok func([]int) bool) *testing.T {
    f := func(pair struct{ N, V int }) bool {
        if pair.N <= 0 {
            return true
        }
        xs := make([]int, pair.N%100)  // cap at 100
        for i := range xs {
            xs[i] = pair.V % (2*pair.N + 1)
        }
        return ok(xs)
    }
    testing.QuickCheck(f)
}

JavaScript + Fast-Check

npm install fast-check

import fc from 'fast-check';

// Core property: sort preserves length and elements
test('sort preserves elements', () => {
  fc.assert(
    fc.property(fc.array(fc.integer()), (xs) => {
      const sorted = [...xs].sort((a, b) => a - b);
      return sorted.length === xs.length
          && sorted.every(v => xs.includes(v));
    }),
    { numRuns: 1000 }
  );
});

// Realistic input: HTTP request shapes
test('request validator accepts valid shapes', () => {
  fc.assert(
    fc.property(
      fc.record({
        method: fc.oneof(
          fc.constant('GET'), fc.constant('POST'),
          fc.constant('PUT'), fc.constant('DELETE')
        ),
        path: fc.string({ minLength: 1, maxLength: 200 }),
        headers: fc.dictionary(
          fc.string({ minLength: 1, maxLength: 50 }),
          fc.string({ minLength: 0, maxLength: 500 })
        ),
        body: fc.oneof(
          fc.constant(null),
          fc.record({ id: fc.integer(), data: fc.json() }),
        ),
      }),
      (req) => {
        const errors = validateRequest(req);
        return errors.length === 0; // should have no errors
      }
    ),
    { numRuns: 500 }
  );
});

Identifying Good Properties

1. Invariants (Never Change)

✓ "sum(xs) + sum(ys) == sum(xs ++ ys)"
✓ "len(sorted(xs)) == len(xs)"
✓ "all even numbers remain even after transform(x)"

2. Equivalences (Two Paths, Same Result)

✓ "parse(serialize(obj)) == obj"
✓ "hash(original) == hash(deserialized)"
✓ "f(a,b) == f(b,a)"  (commutativity)

3. Idempotence (Applying Twice == Applying Once)

✓ "unique(unique(xs)) == unique(xs)"
✓ "sort(sort(xs)) == sort(xs)"
✓ "normalize(normalize(x)) == normalize(x)"

4. Monotonicity / Directionality

✓ "if x > y then f(x) > f(y)"  (monotone increasing)
✓ "if x >= 0 then sqrt(x) >= 0"

5. Round-trip (Encode → Decode)

✓ "JSON.parse(JSON.stringify(obj)) == obj"
✓ "base64_decode(base64_encode(bytes)) == bytes"
✓ "gzip_decompress(gzip_compress(data)) == data"

Anti-Patterns (AVOID)

❌ Property too close to implementation

# This just tests that sort() calls sorted() — useless
@given(st.lists(st.integers()))
def test_bad_implementation_tested(xs):
    assert my_sort(xs) == sorted(xs)  # copies builtin

❌ Property with no falsifiable claim

# "sometimes works" is not a property
@given(st.lists(st.integers()))
def test_wrong(xs):
    assert my_sort(xs) is not None  # always passes

❌ No shrink target — unshrunkable failure

# Custom class without known shrinker = messy failure output
@given(st.integers())
def test_bad(x):
    assert MyClass(x).value == x  # MyClass has no shrink strategy

✅ Always pair with classic unit tests for edge cases — PBT handles the long tail, unit tests handle the critical known cases.

Integration with CI/CD

# .github/workflows/pbt.yml
- name: Property-Based Tests
  run: |
    pytest \
      --hypothesis-show-statistics \
      --hypothesis-seed="${{ github.run_id }}" \
      tests/properties/ \
      -v

Key flags:

• --hypothesis-show-statistics — see distribution of generated inputs
• --hypothesis-seed=X — reproducible runs from a seed
• --hypothesis-database — persist failing examples across runs

Counterexample → Minimal Test Case Workflow

When Hypothesis finds a failure:

1. Copy the minimal shrunk example from the output
2. Create a regression unit test with the counterexample
3. Add it to your test suite so it never regresses

# Regression test created from counterexample
def test_regression_edge_case_387():
    """Regression: empty list with max_value=0 caused crash"""
    assert safe_divide(10, 0) == float('inf')  # or appropriate behavior

@given(st.lists(st.integers(max_value=0)))
def test_empty_edge_case(xs):
    """Counterexample from failed property run"""
    result = process(xs)
    assert result is not None  # or whatever invariant held

Last updated: 2026-03-28