/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 | **unbounded input domain** with universal invariant. LOW value: simple CRUD | UI logic | external API integrations | **closed-world finite domain** (use parametrize instead — see falsifier-gate below). PBT complements example-based testing, doesn't replace it.

Falsifier-gate: closed-world finite → parametrize, NOT PBT

If the input domain is **finite + enumerable** (N known files, M known event types, K known skill names, fixed Python versions), PBT is the wrong tool:

• `Hypothesis` import (~457ms) + per-example bookkeeping > `@pytest.mark.parametrize` overhead
• Shrinking is irrelevant — the failing input is already a known list member, no minimization needed
• Coverage is bounded by the parameter list, not the example budget — fewer assertions, same coverage

**Decision rule**: enumerate the domain. If listable (`[a, b, c, ...]`), use parametrize-collapse or dict-iteration (see `nw-test-optimization` §3.1, §3.2). Reserve PBT for "for all X in DOMAIN, P(X) holds" where DOMAIN is infinite (all strings, all integers, all valid JSON, all sorted lists).

**Empirical anchor 2026-05-18**: 155-file closed-world skill registry PBT migration was correctly aborted at recon stage by the falsifier-gate. Solution: set-difference parametrize-collapse (commit `c2637f6c8`), 5.42s → 0.71s (8.9× faster). Mass-migrating closed-world tests to PBT would have made the suite **slower**, not faster.

See `nw-test-optimization` §4-bis Paradigm-Match Decision Rule for the full shape-to-paradigm table.

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 no