Nw Property Based Testing

---
name: nw-property-based-testing
description: Property-based testing strategies, mutation testing, shrinking, and combined PBT+mutation workflow for test quality validation
user-invocable: false
disable-model-invocation: true
---

# Property-Based Testing and Mutation Testing

> Deferred to Phase 2.25: Mutation testing runs ONCE per feature as final quality gate at orchestrator Phase 2.25 (after all steps complete). Do NOT run mutation testing during inner TDD loop.

## Property-Based Testing (PBT)

Instead of examples ("given X, expect Y"), write properties ("for all valid inputs, condition Z holds").
Framework generates hundreds/thousands of inputs checking property. Dramatically expands test coverage.

## Property Patterns
1. **Invariants**: "for all inputs, condition holds" (sorted list is ordered, balance >= 0)
2. **Roundtrip**: "encode then decode = original" (serialize/deserialize, compress/decompress)
3. **Oracle**: "compare against reference implementation" (optimized vs correct-but-slow)
4. **Metamorphic**: "different operations, same result" (add(a,b)==add(b,a), filter can't increase size)

## Shrinking

When property fails, framework auto-finds minimal failing input. Dramatically accelerates debugging.
Algorithm: find failing input -> try simpler variants -> if still fails, use as new candidate -> repeat.

## PBT Tools by Language

| Language | Framework |
|----------|-----------|
| Python | Hypothesis |
| JavaScript/TypeScript | fast-check |
| Haskell | QuickCheck |
| Rust | quickcheck |
| Java | jqwik |
| C# | FsCheck |

Adopted by Amazon, Volvo, Stripe, Jane Street (ICSE 2024 study).

## When PBT Adds Value
HIGH value: algorithms | data structures | serialization | business rules (validation, calculations) | protocols/state machines.
LOW value: simple CRUD | UI logic | external API integrations.
PBT complements example-based testing, doesn't replace it.

## PBT + TDD Integration
1. Start with example-based TDD for specific cases (drives detailed design)
2. Once basic implementation works, write properties to generalize
3. If property fails: found bug or need refined implementation
4. Refactor freely - properties verify behavior preservation

Properties = higher-level spec that survives refactoring better than examples.

## Mutation Testing

Evaluates test suite quality by introducing artificial bugs (mutations) and checking if tests catch them.
Mutation score = killed mutants / total mutants. Stronger metric than code coverage.

## Mutation Score Targets

| Score | Quality |
|-------|---------|
| < 60% | Weak suite, significant gaps |
| 60-80% | Moderate, some gaps |
| > 80% | Strong, few gaps |

Target: 75-80% minimum. Not all survivors indicate bad tests (equivalent mutants exist).

## Mutation Operators
Change == to != | + to - | remove method call | change constant | modify loop boundary | alter comparison.

## Mutation Testing Tools

| Language | Tool |
|----------|------|
| Java | PIT |
| JavaScript/TypeScript/C# | Stryker |
| Python | mutmut, Cosmic Ray |

Computationally expensive. Use incremental: on changed code in PRs, full codebase weekly.

## Combined PBT + Mutation Workflow
1. Write example-based tests (TDD) -> cover known scenarios
2. Apply mutation testing -> identify assertion gaps -> write more tests
3. Add PBT for complex logic -> cover input space systematically
4. Mutation testing again -> verify properties are comprehensive

Quality ratchet: each technique exposes gaps others miss. Prioritize critical paths and complex algorithms.

## PBT Performance Guidance
- Fast feedback: ~100 examples | CI/CD: ~1000 examples | Nightly builds: ~10000+ examples

Modern frameworks allow configuring example count per context.

## State-Delta + Hypothesis Integration

Combines the delta-first paradigm (see `nw-tdd-methodology::Delta-First Test Paradigm`) with Hypothesis shrinking to cover production code that branches on input shape.

### `path_strategy()` — composite Hypothesis strategy

Location: `nwave_ai/state_delta/strategies/path_strategy.py`

Generates realistic PATH string shapes covering 4 production branches:
1. Empty string (no PATH set)
2. `$HOME/bin` literal (unexpanded shell variable)
3. Legacy fallback path (`/usr/local/bin` only)
4. Idempotent case (target already present in PATH)

**Lazy-import boundary**: `hypothesis` is NOT imported at `import nwave_ai.state_delta.matcher` time. It is loaded only when `path_strategy()` is called. This is verified by a subprocess-isolated test at `tests/state_delta/unit/test_lazy_import.py` — importing the matcher in a hypothesis-free environment must not raise `ImportError`.

### Integration pattern

```python
from hypothesis import given, settings
from nwave_ai.state_delta.strategies.path_strategy import path_strategy
from nwave_ai.state_delta import assert_state_delta, prepended_with, unchanged

@given(path_strategy())
@settings(max_examples=500)
def test_path_injection_all_shapes(initial_path):
    before = {"env.PATH": initial_path, "env.OTHER": "x"}

    result_path = inject_nwave_bin(initial_path)

    after = {"env.PATH": result_path, "env.OTHER": "x"}

    assert_state_delta(
        before,
        after,
        universe={"env.PATH", "env.OTHER"},
        expected={"env.PATH": prepended_with("/home/user/.nwave/bin"),
                  "env.OTHER": unchanged()},
    )
```

Hypothesis shrinking finds the minimal failing PATH shape automatically when a branch is broken.

### When to use this combination

- Production code has **multiple branches over input shape** (empty vs. populated, legacy vs. current format).
- You want both shrinking (Hypothesis strength) and surrounding-state verification (delta-first strength).
- Single `@given` replaces N parametrized example tests covering the same branches.

### Reference

- D-12 Part B hard gate: `tests/state_delta/integration/test_pilot_bug48.py::test_pilot_bug48_post_fix_validated` — 500 examples, GREEN in 0.88s.