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radar-tinder-mobileapp-testing

针对使用Expo + React Native、Firebase（身份验证、Firestore、实时/云函数）、地图API以及设备上的ONNX推理构建的Radar Tinder移动应用程序的测试和验证工作流。此技能应用于回归测试、功能验证、实时问题、排行榜不一致、导航错误以及AI诊断验证。

person作者: jakexiaohubgithub
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Radar Tinder – Mobile App Testing Skill

When to use this skill

Use this skill when:

  • Adding or modifying features in the mobile app
  • Investigating leaderboard, points, or ranking inconsistencies
  • Debugging Firebase Auth, Firestore, or realtime update issues
  • Verifying radar report & confirmation flows
  • Testing navigation, nearby radar discovery, or directions logic
  • Validating on-device ONNX AI inference (offline & online)

Quick Smoke Test (5–10 minutes)

These checks must pass before deeper testing:

  1. App builds and launches (Expo / EAS)
  2. Firebase Auth initializes without errors
  3. Login / signup works (email, Google, Apple if enabled)
  4. Firestore connection established
  5. Leaderboard screen loads without crash

If any fail → check logs, Firebase config, and environment variables first.

Test Layers

A) Manual Smoke Tests (Happy Path)

1. Authentication & Profile

  • Sign up with email
  • Profile document is created in Firestore
  • Username is unique
  • Display name defaults correctly
  • Avatar update reflects in leaderboard

2. Radar Reporting

  • Create a radar report (location + type)
  • Firestore document created
  • Reporter receives points
  • UI reflects updated points

3. Confirmations

  • Second user confirms radar
  • Confirmation stored in Firestore
  • Points awarded correctly
  • Duplicate confirmations prevented

4. Leaderboard & Realtime

  • Leaderboard updates after report/confirmation
  • Changes propagate without app restart
  • Ordering is correct (points / rank)

5. Navigation & Maps

  • Directions API returns valid route
  • Nearby radars load from Firestore
  • Radar alerts appear at correct distances
  • Map markers update correctly

6. AI Diagnostics (ONNX)

  • Sample image runs inference successfully
  • Output labels/confidence are readable
  • Works offline (no network dependency)
  • No UI freeze during inference

Firebase-Specific Validation

Firestore Expectations

  • Collections are structured consistently
  • Writes are idempotent where needed
  • Security rules allow valid actions only
  • No client-side rank calculation

Realtime / Updates

  • Firestore listeners are scoped correctly
  • No duplicate subscriptions
  • Updates clean up on unmount

Auth

  • Firebase Auth state syncs correctly
  • Token refresh does not break sessions
  • Sign-out clears local state

Common Debugging Playbooks

“Leaderboard not updating”

  1. Firestore listener active?
  2. Query orderBy / limit correct?
  3. Security rules blocking reads?
  4. Local state cache stale?

“Points not awarded”

  1. Report written successfully?
  2. Cloud Function triggered?
  3. Transaction failed silently?
  4. Duplicate prevention logic firing?

“Login works but profile missing”

  1. Auth success but Firestore write failed?
  2. Network race condition?
  3. Security rules deny create?

Test Data Strategy

Use consistent test fixtures:

  • 2 test users (reporter / confirmer)
  • 2–3 radar reports
  • 1 confirmation
  • 1 sample image for AI
  • 1 directions route fixture

How Copilot should respond when using this skill

For each test or bug request:

  1. Identify affected flow (Auth / Radar / Confirm / Leaderboard / Maps / AI)
  2. Provide minimal reproduction steps
  3. Suggest fix hypothesis
  4. Recommend tests to add/update
  5. Output verification checklist

Output Format

Copilot responses should follow:

  • Reproduction Steps
  • Expected vs Actual
  • Root Cause Hypothesis
  • Fix Plan
  • Tests Added or Updated
  • How to Verify