Code review process

Code quality assurance is an indispensable part of frontend development, and the code review process is one of the core means to ensure code quality. Through standardized review mechanisms, teams can promptly identify potential issues, unify code styles, and enhance overall collaboration efficiency.

Core Objectives of Code Review

The core objectives of Code Review extend beyond merely identifying errors; they also include knowledge sharing, standardization, and performance optimization. Specifically, these objectives can be broken down into the following aspects:

1. Functional Verification: Ensure the code implements the required functionality correctly and logically.
2. Code Readability: Clear variable naming, reasonable structure, and adequate comments.
3. Performance Optimization: Avoid redundant calculations, memory leaks, and other issues.
4. Security Checks: Prevent common frontend vulnerabilities like XSS and CSRF.
5. Maintainability: Code should be easy to extend and modify.

Designing the Code Review Process

1. Pre-Submission Self-Check

Developers should complete the following self-check steps before submitting code:

• Run unit tests and integration tests.
• Use tools like ESLint to check code style.
• Ensure no debug code (e.g., console.log) is accidentally submitted.

Example: Configuring the .eslintrc.js file

module.exports = {
  rules: {
    'no-console': 'error',
    'indent': ['error', 2],
    'quotes': ['error', 'single']
  }
};

2. Choosing Review Tools

Common tool combinations:

• Git Platforms: Pull Request features in GitHub/GitLab
• Automated Checks: Integration with ESLint, Prettier, Jest, etc.
• Manual Review: Participation by at least 1-2 team members

3. Organizing Review Meetings

• Frequency: Daily or weekly at fixed times
• Duration: No longer than 1 hour per session
• Participants: Relevant module owners + randomly selected team members

Practical Code Review Practices

1. Review Checklist

Suggested items:

• [ ] Functions exceeding 50 lines
• [ ] Presence of magic numbers
• [ ] Error handling for API calls
• [ ] Mobile responsiveness considerations

2. Examples of Common Issues

Problematic Code:

// Hardcoded API URL
function fetchData() {
  return fetch('http://production-api.com/data')
    .then(res => res.json());
}

Improved Solution:

// Using environment variables
function fetchData() {
  const API_HOST = process.env.API_HOST || 'https://default-api.com';
  return fetch(`${API_HOST}/data`)
    .then(res => {
      if (!res.ok) throw new Error('Network response was not ok');
      return res.json();
    })
    .catch(error => {
      console.error('Fetch error:', error);
      throw error;
    });
}

3. Expressing Review Feedback

Avoid vague statements; be specific:

• ❌ "This function is poorly written"
• ✅ "There are three nested if statements in this function; consider refactoring with the strategy pattern"

Integrating Automated Reviews

1. Configuring Git Hooks

Automate checks with pre-commit hooks:

#!/bin/sh
npm run lint && npm test

2. CI/CD Pipeline Example

GitLab CI configuration example:

stages:
  - lint
  - test
  - deploy

lint_code:
  stage: lint
  script:
    - npm run lint

unit_test:
  stage: test
  script:
    - npm run test:cov

Handling Special Scenarios

1. Emergency Hotfixes

• Allow simplified processes but require post-review
• Label with [HOTFIX] for identification
• Complete full review within 24 hours of the fix

2. Large-Scale Refactoring

• Break into smaller PRs for incremental reviews
• Prepare a refactoring design document in advance
• Schedule dedicated review sessions

Cultivating a Review Culture

1. Positive Feedback: Explicitly praise well-written code
2. Learning Cases: Regularly share notable review examples
3. Rotation System: Ensure all team members participate in reviews
4. Metrics Tracking: Record defect type distributions from reviews

Common Anti-Patterns and Solutions

1. Perfunctory Reviews:

   • Symptom: Only checking code formatting, not logic
   • Solution: Require reviewers to run the code

2. Over-Reviewing:

   • Symptom: Excessive debate over non-critical details
   • Solution: Establish a code style guide as a reference

3. Review Delays:

   • Symptom: PRs left unattended for long periods
   • Solution: Set SLAs (e.g., must respond within 24 hours)

Tracking and Analyzing Review Metrics

Recommended data to track:

• Average review time per PR
• Defect discovery rate post-review
• Top 5 common defect types
• Adoption rate of review suggestions

Example dashboard:

| Month | PRs Reviewed | Avg. Time | Defect Rate |
|-------|-------------|----------|------------|
| Jan   | 42          | 2.3h     | 18%        |
| Feb   | 56          | 1.8h     | 12%        |

Toolchain Expansion Suggestions

1. Code Visualization:

   • Use CodeScene for code evolution analysis
   • Detect code smells with SonarQube

2. AI Assistance:

   • GitHub Copilot's review suggestions
   • Automated optimizations with Amazon CodeGuru

3. Custom Rules:

   • Write ESLint plugins for business-specific patterns
   • Create code template libraries to reduce repetitive work