# Nw Test Optimization Paradigm Match

> Decision rule matching test SHAPE to the right paradigm before authoring/migrating - closed-world vs multi-step-setup vs state-mutation vs unbounded-invariant vs few-examples, plus the falsifier-gate that blocks PBT on finite domains

- Skill: `nwave-ai/nw-test-optimization-paradigm-match` (Agent Skill)
- Install (CLI): `npx skillmds@latest add nwave-ai/nw-test-optimization-paradigm-match`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nwave-ai/nw-test-optimization-paradigm-match/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: nWave-ai (https://skillmd.com/u/nwave-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/nwave-ai/nw-test-optimization-paradigm-match

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# Paradigm-Match Decision (KNOWLEDGE)

**Kind**: KNOWLEDGE (decision rule / taste). **One trigger**: "which test paradigm fits this test shape — before I author or migrate" — fires at the recon stage of any optimization/authoring where the paradigm is not yet fixed. Mismatched paradigm = ceremony without value (or correctness loss). Composed by `nw-test-optimization`.

## Decision table

| Test shape | Paradigm | Empirical anchor |
|---|---|---|
| **Closed-world finite input** (N known files, M known event types, K known skill names) — assertion shape identical across instances | Parametrize-collapse → consolidation §3.1 / Dict-iteration → §3.2 | `c2637f6c8` set-difference 155-test → 1 (8.9× faster) |
| **Multi-step contract on shared expensive setup** — independent assertions on post-setup read-only state | Single-lifecycle consolidation → §3.7 | `defc07f0d` 24-test 152s → 63s (2.4× faster) |
| **User-observable state mutation** (installer/uninstaller/sync/hooks/settings) — N tests verifying same lifecycle's side effects | State-delta paradigm → §3.8 | Task #12 pilot (13% compression / 17% wall-clock) |
| **Unbounded input domain** with universal invariant (algorithm, serialization, business rule) — "for all X in DOMAIN, P(X) holds" | Property-based testing (Hypothesis) — see `nw-property-based-testing` | Standard PBT literature; nWave-internal scope = unbounded ONLY |
| **Single happy-path + 1-3 sad paths** with distinct error messages | Example-based unit tests, no consolidation needed | n/a — already minimal |

## Falsifier-gate before adopting PBT

Closed-world finite input is NOT PBT territory. Hypothesis import (~457ms) + per-example bookkeeping is **slower** than `@pytest.mark.parametrize` when the input set is finite + enumerable. Apply the gate:

1. **Enumerate the input domain**. Is it finite + listable (`SKILL_NAMES_149`, `EVENT_TYPES_5`, `SUPPORTED_PYTHONS_3`)? → parametrize-collapse, NOT PBT.
2. **Is the invariant value-independent** (holds for ANY valid X, not specific Xs)? → PBT candidate.
3. **Run cost-benefit**: if domain ≤ 10× the typical PBT example budget (100), parametrize wins on wall-clock + readability + shrinking-from-trivial-counterexamples cost.

**Empirical anchor 2026-05-18**: PBT migration attempt on 155-file closed-world skill registry was correctly aborted at recon stage by the falsifier-gate. Solution was set-difference parametrize-collapse (`c2637f6c8`, 5.42s → 0.71s, 8.9× faster). Documented in memory `feedback_state_transition_test_paradigm` (revised 2026-05-05).

## Decision tree (concise)

```
Test shape?
├─ Same assertion, varying inputs from finite known set?
│   └─ parametrize-collapse (§3.1) OR dict-iteration (§3.2)
├─ N independent assertions on same post-setup read-only state?
│   └─ single-lifecycle consolidation (§3.7)
├─ State-mutation lifecycle with delta assertions?
│   └─ state-delta paradigm (§3.8)
├─ Universal invariant over unbounded domain?
│   └─ PBT (nw-property-based-testing)
└─ Few specific examples with distinct outcomes?
    └─ example-based, no consolidation
```

The consolidation mechanics (§3.x) live in `nw-test-optimization-consolidation`; PBT lives in `nw-property-based-testing`.