# TASK-096-02-02: Tests and Docs Confidence Scoring Normalization Across Engines

**Parent:** [TASK-096-02](./TASK-096-02_Confidence_Scoring_Normalization_Across_Engines.md)  
**Status:** ✅ Done  
**Priority:** 🔴 High  
**Depends On:** [TASK-096-02-01](./TASK-096-02-01_Core_Confidence_Scoring_Normalization_Engines.md)

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## Objective

Add tests and documentation updates for **Confidence Scoring Normalization Across Engines**.

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## Repository Touchpoints

- `tests/unit/`
- `_docs/`

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## Planned Work

### Regression Scenarios (Required)

1. matcher normalization path: semantic and ensemble matcher outputs map into one shared normalized confidence envelope.
2. override/firewall normalization path: override and firewall confidence signals use the same confidence scale and provenance fields.
3. missing-signal path: unavailable, malformed, or partial engine scores fall back deterministically to the expected normalized state.
4. cross-engine consistency path: identical semantic certainty from different engines yields equivalent normalized confidence classes.

### Metrics To Capture

- normalization coverage by source engine (semantic, ensemble, override, firewall)
- malformed-input normalization fallback count and handling outcome
- cross-engine confidence-class consistency rate

### Documentation Deliverables

- update task-linked docs with a before/after summary tied to the captured metrics
- document exact test commands, fixtures, and profile/config used during validation
- record compatibility or migration notes when behavior differs between surfaces

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## Acceptance Criteria

- all required regression scenarios are implemented and passing in CI/local test runs
- metrics are captured with baseline vs post-change values and attached to the task update
- docs include the regression matrix and explain expected behavior boundaries
- no untracked regressions are observed on related matcher/override/firewall confidence paths

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## Atomic Work Items

1. Implement the required regression scenarios in focused unit/integration tests.
2. Run the target suites, collect metric outputs, and compare to baseline values.
3. Update docs with regression matrix, metric table, and migration/compatibility notes.
4. Verify adjacent surfaces for spillover regressions and document the result.
