Unit test PeerMentorStatsAggregator threshold logic
epic-achievement-badges-services-task-014 — Write unit tests for PeerMentorStatsAggregator covering: correct 3rd and 15th assignment threshold detection, streak length computation for consecutive and broken week sequences, training completion counting, and that queries use mentor_id + date index paths. Mock Supabase client. Target 90%+ branch coverage on business logic methods.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 3 - 413 tasks
Can start after Tier 2 completes
Implementation Notes
Structure the test file with one top-level group per public method of PeerMentorStatsAggregator. For streak computation, construct date sequences programmatically (e.g., `DateTime.utc(2025, 1, 6)` for a Monday) to avoid fragile hardcoded dates. For Supabase query verification, capture the query parameters in the mock and assert on the filter keys — do not assert on SQL strings. If PeerMentorStatsAggregator is not currently injectable (hardcodes Supabase client), refactor the constructor to accept a SupabaseClient parameter before writing tests.
Consider extracting streak and threshold logic into package-private pure functions to make them directly testable without any mock setup.
Testing Requirements
Pure unit tests using flutter_test. Create a MockSupabaseClient using mockito or manual fake implementing the Supabase query builder interface. Use table-driven test patterns (parameterized inputs) for threshold boundary tests to maximise coverage with minimal boilerplate. Generate coverage report with `flutter test --coverage` and verify lcov report shows 90%+ on lib/src/services/peer_mentor_stats_aggregator.dart.
Group tests by method under descriptive group() blocks.
peer-mentor-stats-aggregator must compute streaks and threshold counts across potentially hundreds of activity records per peer mentor. Naive queries (full table scans or N+1 patterns) will cause slow badge evaluation, especially when triggered on every activity save for all active peer mentors.
Mitigation & Contingency
Mitigation: Design aggregation queries using Supabase RPCs with window functions or materialised views from the start. Add database indexes on (peer_mentor_id, activity_date, activity_type) before writing any service code. Profile all aggregation queries against a dataset of 500+ activities during development.
Contingency: If query performance is insufficient at launch, implement incremental stat caching: maintain a peer_mentor_stats snapshot table updated on each activity insert via a database trigger, so the aggregator reads from pre-computed values rather than scanning raw activity rows.
badge-award-service must be idempotent, but if two concurrent edge function invocations evaluate the same peer mentor simultaneously (e.g., from a rapid double-save), both could pass the uniqueness check before either commits, resulting in duplicate badge records.
Mitigation & Contingency
Mitigation: Rely on the database-level uniqueness constraint (peer_mentor_id, badge_definition_id) as the final guard. In the service layer, use an upsert with ON CONFLICT DO NOTHING and return the existing record. Add a Postgres advisory lock or serialisable transaction for the award sequence during the edge function integration epic.
Contingency: If duplicate records are discovered in production, run a deduplication migration to remove extras (keeping earliest earned_at) and add a unique index if not already present. Alert engineering via Supabase database webhook on constraint violations.
The badge-configuration-service must validate org admin-supplied criteria JSON on save, but the full range of valid criteria types (threshold, streak, training-completion, tier-based) may not be fully enumerated during development, leading to either over-permissive or over-restrictive validation that frustrates admins.
Mitigation & Contingency
Mitigation: Define a versioned Dart sealed class hierarchy for CriteriaType before writing the validation logic. Review the hierarchy with product against all known badge types across NHF, Blindeforbundet, and HLF before implementation. Build the validator against the sealed class so new criteria types require an explicit code addition.
Contingency: If admins encounter validation rejections for legitimate criteria, expose a 'criteria_raw' escape hatch (JSON passthrough, admin-only) with a product warning, and schedule a sprint to formalise the new criteria type properly.