Error tracking process

Code quality assurance and error tracking processes are indispensable in frontend development. High-quality code enhances the maintainability and stability of applications, while a robust error tracking mechanism enables quick identification and resolution of issues, minimizing the impact of production failures.

Core Practices for Code Quality Assurance

Ensuring code quality requires a multi-dimensional approach, including coding standards, static analysis, unit testing, and code reviews. Below are some specific practices:

Coding Standards and Style Guides

Establishing unified coding standards is the first step in ensuring code quality. For example, using ESLint with Prettier can automate code formatting:

// .eslintrc.js
module.exports = {
  extends: ['eslint:recommended', 'plugin:prettier/recommended'],
  rules: {
    'no-console': 'warn',
    'no-unused-vars': 'error'
  }
};

Teams should customize standards based on project characteristics, such as:

• Component naming in PascalCase
• Variable naming in camelCase
• Constants in uppercase with underscores
• Prohibiting direct modification of props

Static Code Analysis

Static analysis tools can identify issues before code execution. TypeScript is an excellent type-checking tool:

interface User {
  id: number;
  name: string;
}

function getUserName(user: User): string {
  return user.name;
}

// Compilation error
getUserName({ id: 1 }); // Missing 'name' property

Other commonly used tools include:

• ESLint: JavaScript/TypeScript syntax checking
• Stylelint: CSS/SCSS style checking
• SonarQube: Comprehensive code quality detection

Unit Testing and Coverage

A robust test suite effectively prevents regression issues. Example of testing a React component with Jest:

// Button.test.js
import { render, screen } from '@testing-library/react';
import Button from './Button';

test('renders button with correct text', () => {
  render(<Button>Click me</Button>);
  expect(screen.getByText(/click me/i)).toBeInTheDocument();
});

test('calls onClick when clicked', () => {
  const handleClick = jest.fn();
  render(<Button onClick={handleClick} />);
  userEvent.click(screen.getByRole('button'));
  expect(handleClick).toHaveBeenCalledTimes(1);
});

Recommended test coverage targets:

• Statement coverage >80%
• Branch coverage >70%
• Function coverage >90%

Code Review Process

Code Review (CR) is the final line of defense for quality assurance. Effective CR should:

1. Limit each PR to under 400 lines of code.
2. Focus on:
   • Correctness of business logic
   • Potential performance issues
   • Edge case handling
   • Code readability
3. Use templates to ensure consistency:

## Change Description
[Describe the background and purpose of the changes]

## Test Validation
[List executed test cases]

## Impact Scope
[Specify affected functional modules]

Error Tracking Process Design

A robust error tracking system helps teams respond quickly to production issues. Key steps include:

Error Collection and Reporting

Frontend errors fall into several categories:

1. JavaScript runtime errors
2. Resource loading failures
3. API request exceptions
4. User behavior anomalies

Example of error collection using Sentry:

import * as Sentry from '@sentry/browser';

Sentry.init({
  dsn: 'https://example@sentry.io/123',
  release: '1.0.0',
  environment: 'production'
});

// Manual error capture
try {
  riskyOperation();
} catch (err) {
  Sentry.captureException(err);
  console.error(err);
}

// Global error handling
window.addEventListener('error', (event) => {
  Sentry.captureException(event.error);
});

Error Classification and Prioritization

Establish error severity levels:

Error Diagnosis and Resolution

Typical diagnosis workflow:

1. Reproduce the issue:

   • Collect user environment details (browser, OS, network, etc.)
   • Check if specific users are affected
   • Attempt reproduction in different environments

2. Analyze logs:

   // Enhance error context
   Sentry.configureScope((scope) => {
     scope.setUser({ id: user.id });
     scope.setTag('page', window.location.pathname);
     scope.setExtra('apiResponse', apiResponse);
   });
   

3. Validate fixes:

   • Write regression tests
   • Verify in staging environment
   • Monitor post-fix error rates

Error Retrospectives and Prevention

Conduct regular error retrospectives:

1. Root cause analysis (5 Whys method)
2. Implement preventive measures:
   • Add test cases
   • Improve monitoring metrics
   • Modify development processes
3. Update error handling guidelines

Monitoring and Alerting Systems

Real-time monitoring is critical for error tracking. Key frontend metrics:

Performance Metrics

• FCP (First Contentful Paint)
• LCP (Largest Contentful Paint)
• CLS (Cumulative Layout Shift)
• TTI (Time to Interactive)

Business Metrics

• Key button click-through rates
• Page conversion rates
• API success rates

Example of Prometheus alert rules:

groups:
- name: frontend.rules
  rules:
  - alert: HighErrorRate
    expr: rate(frontend_errors_total[5m]) > 0.1
    for: 10m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"
      description: "Error rate is {{ $value }} per second"

CI/CD Pipeline Integration

Integrate quality checks into CI/CD pipelines:

# .github/workflows/ci.yml
name: CI Pipeline
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - run: npm install
    - run: npm run lint
    - run: npm test -- --coverage
    - uses: codecov/codecov-action@v1

  deploy:
    needs: test
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - run: npm install
    - run: npm run build
    - uses: actions/upload-artifact@v2
      with:
        name: production-build
        path: build/

Key checkpoints:

1. Code formatting validation
2. Type checking
3. Unit testing
4. E2E testing
5. Build artifact analysis
6. Dependency security checks

Recommended Error Tracking Toolchain

Comprehensive quality assurance toolchain:

Frontend Error Handling Best Practices

Robust error handling strategies:

1. Component-level error boundaries

class ErrorBoundary extends React.Component {
  state = { hasError: false };

  static getDerivedStateFromError() {
    return { hasError: true };
  }

  componentDidCatch(error, info) {
    Sentry.captureException(error, { extra: info });
  }

  render() {
    if (this.state.hasError) {
      return <FallbackUI />;
    }
    return this.props.children;
  }
}

2. API request retry mechanism

async function fetchWithRetry(url, options = {}, retries = 3) {
  try {
    const response = await fetch(url, options);
    if (!response.ok) throw new Error(response.statusText);
    return response.json();
  } catch (err) {
    if (retries <= 0) throw err;
    await new Promise((r) => setTimeout(r, 1000 * (4 - retries)));
    return fetchWithRetry(url, options, retries - 1);
  }
}

3. User behavior tracking

// Track critical actions
function trackAction(action, payload) {
  analytics.track(action, payload);
  if (payload.error) {
    Sentry.addBreadcrumb({
      category: action,
      data: payload,
      level: 'error'
    });
  }
}

Quality Metrics Framework

Establish quantifiable quality assessment:

1. Defect density = Defect count / KLOC (thousand lines of code)
2. Mean Time To Repair (MTTR)
3. Production incident rate
4. Test coverage trends
5. Static analysis warnings
6. Build success rate

Visualize metrics using dashboards:

// Grafana dashboard example
const dashboard = {
  panels: [
    {
      title: 'Error Rate',
      type: 'graph',
      targets: [
        {
          expr: 'rate(frontend_errors_total[1h])',
          legendFormat: 'Error Rate'
        }
      ]
    },
    {
      title: 'Test Coverage',
      type: 'stat',
      targets: [
        {
          expr: 'coverage_percentage',
          format: 'percent'
        }
      ]
    }
  ]
};