Cognitive Accessibility Audit utility implementation
epic-cognitive-accessibility-foundation-task-011 — Build the CognitiveAccessibilityAudit utility class that performs a full accessibility audit of a widget tree or screen configuration. Integrates with CognitiveLoadRuleEngine to validate step counts, choice counts, and CTA constraints. Produces a structured audit report with severity levels (error, warning, info) and plain-language remediation suggestions. Used in development tooling and CI pipeline checks.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 3 - 413 tasks
Can start after Tier 2 completes
Implementation Notes
Keep auditScreen() as a pure synchronous function delegating to CognitiveLoadRuleEngine.instance.validateScreen() and augmenting the result list with structural checks (e.g., missing page title, no back button declared). For auditWidgetTree(), use the Flutter test framework's element tree APIs (WidgetTester.allElements or find.byType) — this is only valid inside a testWidgets() context. To make the utility usable from a standalone test file (not requiring a running widget tree), provide auditScreen() as the primary CI-facing API; auditWidgetTree() is a bonus for in-test usage. Remediation strings should be defined as a Map
The toJson() method should produce a flat, human-readable structure (not deeply nested) so CI log parsers can grep for 'isCompliant': false. Consider adding a printReport() helper that formats the report for console output with ANSI colours (errors in red, warnings in yellow) for developer DX.
Testing Requirements
Unit tests with dart:test for auditScreen() — test compliant config returns isCompliant=true with empty violations; test each rule violation produces correct severity, message, and remediation. Widget tests with flutter_test for auditWidgetTree() — build a test widget with a known unlabelled Semantics node and verify the report flags it. Test toJson() roundtrip: serialise and deserialise a report, verify all fields preserved. Test release-mode guard on auditWidgetTree() by mocking kReleaseMode.
Test CI sample file compiles and runs against a compliant ScreenConfig set without failures. Target 90% line coverage across the audit utility classes. Include one negative test: a screen config that violates all 4 cognitive load rules simultaneously — verify report has 4 error-severity violations.
The error message registry and help content registry both depend on bundled JSON assets loaded at startup. If asset loading fails silently (e.g. malformed JSON, missing pubspec asset declaration), the entire plain-language layer falls back to empty strings or raw error codes, breaking the accessibility guarantee app-wide.
Mitigation & Contingency
Mitigation: Implement eager validation of both assets during app initialisation with an assertion failure in debug mode and a structured error log in release mode. Add integration tests that verify asset loading in the Flutter test harness on every CI run.
Contingency: Ship a hardcoded minimum-viable fallback message set directly in Dart code so the app always has at least a safe generic message, preventing a blank or code-only error surface.
The AccessibilityDesignTokenEnforcer relies on dart_code_metrics custom lint rules. If the lint toolchain is not already configured in the project's CI pipeline, integrating a new linting plugin may cause unexpected build failures or require significant CI configuration work beyond the estimated scope.
Mitigation & Contingency
Mitigation: Audit the existing dart_code_metrics configuration in the project before starting implementation. Scope the lint rules to a separate Dart package that can be integrated incrementally, starting with the most critical rule (hard-coded colors) and adding others in subsequent iterations.
Contingency: Fall back to Flutter test-level assertions (using the cognitive-accessibility-audit utility) to catch violations in CI if the lint plugin integration is delayed, preserving enforcement coverage without blocking the epic.
WizardDraftRepository must choose between shared_preferences and Hive for local persistence. Choosing the wrong store for the data volume (e.g. shared_preferences for complex nested wizard state) can lead to serialisation bugs or performance degradation, particularly on lower-end Android devices used by some NHF members.
Mitigation & Contingency
Mitigation: Define a clean repository interface first and implement shared_preferences as the initial backend. Profile serialisation round-trip time with a realistic wizard state payload (≈10 fields) before committing to either store.
Contingency: Swap the persistence backend behind the repository interface without touching wizard UI code, which is possible precisely because the repository abstraction isolates the storage detail.
The AccessibilityDesignTokenEnforcer scope could expand significantly if a large portion of existing widgets use hard-coded values. Discovering widespread violations during this epic would force either a major refactor or a decision to exclude legacy components, potentially reducing the enforcer's coverage and value.
Mitigation & Contingency
Mitigation: Run a preliminary audit of existing widgets using a simple grep for hard-coded hex colors and raw pixel values before implementation begins. Use the results to set a realistic remediation boundary for this epic and log all out-of-scope violations as tracked tech-debt items.
Contingency: Scope the enforcer to new and modified components only (via file-path filters in dart_code_metrics config), shipping a partial but immediately valuable coverage rather than blocking the epic on full-codebase remediation.