Write integration tests for detection logic
epic-organizational-hierarchy-management-duplicate-detection-task-011 — Write integration tests covering the full duplicate detection pipeline: trigger fires on new activity insert, cross-hierarchy comparison identifies matching fingerprints, suspected duplicate record is created, RLS policies correctly scope data per organization, and resolution updates are persisted. Use Supabase local dev environment for test execution.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 5 - 253 tasks
Can start after Tier 4 completes
Implementation Notes
Set up Supabase local with `supabase init` and `supabase start`. Use `supabase/seed.sql` to insert deterministic test organizations, users, and activities. For trigger testing, insert activities directly via the service role client in test setup and assert the suspected_duplicates table state after a brief await (Future.delayed or polling loop with timeout). For RLS testing, create two SupabaseClient instances authenticated with different test JWTs (one per org) and assert query results are scoped correctly.
Use `supabase db reset` between test runs if full isolation is needed. Document the local setup steps in a `INTEGRATION_TEST_SETUP.md` file so any developer can run tests locally.
Testing Requirements
This task IS the testing task. Tests must be written as Flutter integration tests in `integration_test/duplicate_detection/`. Use a test helper class (DuplicateDetectionTestHelper) that wraps Supabase admin client for seeding and cleanup. Each test file should have a setUpAll that seeds the local DB and a tearDownAll that removes test data.
Group tests by concern: trigger tests, RLS tests, resolution workflow tests. Use expect() with meaningful failure messages. Add a CI step in the project's GitHub Actions (or equivalent) workflow that runs `supabase start` and then executes the integration tests.
Fingerprint-based similarity matching may produce high false-positive rates for common activity types (e.g., weekly group sessions with the same participants), causing alert fatigue among coordinators and undermining trust in the detection system.
Mitigation & Contingency
Mitigation: Start with conservative, high-confidence thresholds (exact peer mentor match + same date + same activity type) before adding looser fuzzy matching. Allow NHF administrators to tune thresholds based on observed false-positive rates. Log all detection decisions for retrospective threshold calibration.
Contingency: Introduce a snooze mechanism allowing coordinators to dismiss false positives for a configurable period. Track dismissal rates per activity type and automatically raise the similarity threshold for activity types with high dismissal rates.
A database trigger on the activities insert path adds synchronous overhead to every activity registration. For HLF peer mentors with 380 annual registrations or coordinators doing bulk proxy registration, this could create perceptible latency or lock contention.
Mitigation & Contingency
Mitigation: Implement the trigger as a DEFERRED constraint trigger (fires after the transaction commits) or replace it with a LISTEN/NOTIFY pattern that queues detection work asynchronously via an Edge Function, completely decoupling detection from the registration write path.
Contingency: Disable the synchronous trigger entirely and rely solely on the scheduled Edge Function for batch detection. Accept a detection delay of up to the scheduling interval (e.g., 15 minutes) in exchange for zero impact on registration latency.
The duplicate detection logic must be validated and approved by NHF before go-live, including agreement on threshold values and the review workflow. NHF stakeholder availability for sign-off may delay this epic's release independently of technical readiness.
Mitigation & Contingency
Mitigation: Gate the feature behind the NHF-specific feature flag so technical deployment can proceed independently of business approval. Involve an NHF administrator in threshold calibration sessions during QA, reducing the formal sign-off surface to policy and workflow rather than technical details.
Contingency: Release the detection system in 'silent mode' — flagging duplicates internally without surfacing notifications to coordinators — until NHF approves the workflow. Use the silent period to collect real data on false-positive rates and refine thresholds before activating notifications.