Testing and Debugging Guide

You are assisting with debugging the vehicle insurance data analysis platform (Vue 3 frontend + Flask backend + Pandas data processing).

When to Use This Skill

Activate this skill when the user reports:

• Data not updating or refreshing
• Filters/筛选 not working correctly
• Charts/图表 not displaying
• API errors or slow responses
• Style/样式 rendering issues
• Performance problems
• Build or deployment failures

Quick Diagnostic Workflow

Step 1: Identify the Problem Layer

Ask the user to describe the symptom, then categorize:

Frontend Issues (Vue/UI):

• UI not updating → Check reactive data and computed properties
• Chart not showing → Verify ECharts initialization and data format
• Filters ineffective → Check Store state and API params
• Styles broken → Inspect CSS variables and scoped styles

Backend Issues (Flask/Pandas):

• API errors → Check backend logs (backend/backend.log)
• Slow responses → Profile Pandas operations
• Missing data → Verify CSV file existence and permissions

Integration Issues:

• CORS errors → Check Flask-CORS configuration
• Network failures → Inspect browser Network tab

Step 2: Run Diagnostic Commands

Guide the user through these checks:

For Frontend Issues

# Check if dev server is running
lsof -i :5173

# View browser console
# Open DevTools (F12) → Console tab → Check for errors

# Check Vue DevTools
# Install Vue DevTools extension → Inspect component state

For Backend Issues

# Check if backend is running
lsof -i :5000

# View backend logs
tail -f backend/backend.log

# Test API directly
curl http://localhost:5000/api/latest-date

Step 3: Common Problems & Solutions

Refer to the Common Issues Reference for detailed troubleshooting steps.

Current Project Structure (for context)

项目/
├── frontend/               # Vue 3 + Vite
│   ├── src/
│   │   ├── components/    # KpiCard, FilterPanel, ChartView
│   │   ├── stores/        # data.js, filter.js, app.js (Pinia)
│   │   ├── views/         # Dashboard.vue
│   │   └── services/      # api.js (Axios)
│   └── package.json       # NO testing libraries installed yet
├── backend/
│   ├── api_server.py      # Flask routes
│   ├── data_processor.py  # Pandas logic
│   └── backend.log        # Runtime logs
└── data/                  # CSV files

Important Context:

• Project does NOT currently have Vitest, pytest, or any testing framework installed
• Testing is manual through browser DevTools and curl commands
• No CI/CD pipeline configured

Debugging Strategies by Component

KpiCard Component Issues

Symptom: KPI values not updating

Diagnostic checklist:

1. Open Vue DevTools → Components → Find KpiCard instance
2. Check props: value, trend, loading
3. Verify parent Dashboard component is passing correct data
4. Check DataStore state: store.kpiData

Common causes:

• API returned data but Store didn't update → Check fetchKpiData() action
• Store updated but component didn't re-render → Verify reactive refs
• Data is correct but formatting is wrong → Check valueType prop

FilterPanel Issues

Symptom: Filters applied but data doesn't change

Diagnostic steps:

1. Open Vue DevTools → Pinia → FilterStore
2. Verify activeInstitution, activeTeam, etc. are updated
3. Check if applyFilters() action was triggered
4. Inspect Network tab → Verify API request includes filter params

Quick fix:

// In browser console
const filterStore = useFilterStore()
console.log('Active filters:', filterStore.activeInstitution, filterStore.activeTeam)

const dataStore = useDataStore()
dataStore.fetchFilteredData()  // Force refresh

ChartView Issues

Symptom: Chart not rendering

Diagnostic checklist:

1. Open browser console → Check for ECharts errors
2. Verify chart container has non-zero dimensions
3. Check chartData prop structure matches ECharts format
4. Confirm ECharts instance initialized

Quick fixes:

// In ChartView.vue, add logging
onMounted(() => {
  console.log('Chart container:', chartRef.value)
  console.log('Chart data:', props.chartData)
  console.log('Container size:', chartRef.value?.offsetWidth, chartRef.value?.offsetHeight)
})

Backend API Errors

Symptom: API returns 500 or 404

Diagnostic steps:

1. Check backend/backend.log for Python exceptions
2. Test API endpoint with curl:
   bashCopy

   curl -X GET 'http://localhost:5000/api/kpi?period=day'
   

3. Verify Flask is running: ps aux | grep api_server
4. Check if CSV file exists and is readable

Common causes:

• 车险清单_2025年10-11月_合并.csv not found → Run scan_and_process_new_files()
• Pandas DataFrame empty → Check data cleaning logic in data_processor.py
• Column name mismatch → Verify CSV headers match expected field names

Performance Debugging

Slow Data Loading

Check these:

1. CSV file size → Large files slow Pandas reads
2. Pandas operations → Avoid row-by-row iteration
3. Network latency → Time API requests in Network tab

Optimization tips:

# In data_processor.py
# SLOW (avoid)
for index, row in df.iterrows():
    df.at[index, 'new_col'] = some_function(row)

# FAST (vectorized)
df['new_col'] = df.apply(lambda row: some_function(row), axis=1)

Memory Issues

Symptoms: Browser/Python crashes

Diagnostic:

# Check memory usage
# macOS:
top -o MEM

# Check Python process
ps aux | grep python | awk '{print $11, $6/1024 "MB"}'

Solutions:

• Reduce CSV data loaded into memory
• Clear browser cache
• Restart backend process

Logging Best Practices

Frontend Logging

Current approach (add to components as needed):

// In stores/data.js
export const useDataStore = defineStore('data', {
  actions: {
    async fetchKpiData() {
      console.log('[DataStore] Fetching KPI data...')
      try {
        const response = await api.getKpiData()
        console.log('[DataStore] KPI data loaded:', response.data)
        this.kpiData = response.data
      } catch (error) {
        console.error('[DataStore] Fetch error:', error)
        this.error = error.message
      }
    }
  }
})

Backend Logging

Current configuration (already in place):

# backend/api_server.py uses Python logging
import logging

logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
    handlers=[
        logging.FileHandler('backend/backend.log'),
        logging.StreamHandler()
    ]
)

View logs:

# Real-time monitoring
tail -f backend/backend.log

# Search for errors
grep -i "error" backend/backend.log

# Last 50 lines
tail -n 50 backend/backend.log

Browser DevTools Checklist

Essential Tabs

1. Console: JavaScript errors and log messages
2. Network: API requests/responses, timing, status codes
3. Vue DevTools: Component tree, Pinia stores, events
4. Elements: Inspect DOM and CSS

Common Workflow

1. User reports: "Data not refreshing"
2. Open Console → Check for errors
3. Open Network → Filter by XHR → Check API calls
4. Open Vue DevTools → Pinia → Inspect DataStore state
5. If API failed → Check backend logs
6. If API succeeded but UI not updated → Check component reactive data

Quick Reference Links

• Common Issues Guide - Detailed troubleshooting for 10+ common problems
• vue-component-dev Skill - Component architecture reference
• backend-data-processor Skill - Pandas debugging tips
• api-endpoint-design Skill - API error codes

Testing Future Plans

Note: Project does not currently have automated testing configured.

If user wants to add testing:

1. Frontend: Recommend Vitest + @vue/test-utils
2. Backend: Recommend pytest + pytest-flask
3. Refer to TESTING_SETUP.md for installation guide

Emergency Fixes

Nuclear Option: Full Restart

# Stop all processes
pkill -f api_server
pkill -f vite

# Clear caches
rm -rf frontend/node_modules/.vite
rm -rf frontend/dist

# Restart backend
cd backend && python api_server.py &

# Restart frontend
cd frontend && npm run dev

Data Corruption Recovery

# Backup current data
cp 车险清单_2025年10-11月_合并.csv 车险清单_backup.csv

# Re-scan and rebuild
# (Python)
from data_processor import DataProcessor
processor = DataProcessor()
processor.scan_and_process_new_files()

Summary

This skill focuses on practical debugging for the current project state. It assumes:

• No testing framework installed (manual testing only)
• Simple deployment (no Docker/Kubernetes)
• Standard Vue 3 + Flask stack

For advanced testing setup, refer to companion guides. For deployment debugging, use the deployment-and-ops skill.

Key principle: Always start with logs (browser Console + backend.log) before diving into code.