Supercharge Your CI/CD with AI

ci_cd_overview.md

Ready to take your CI/CD pipelines to the next level? If you've been using Cursor AI and Windsurf AI for coding, you're in for a treat. These AI-powered editors can help you generate code, tests, and deployment scripts, making your continuous integration and continuous deployment processes faster and more reliable. In this tutorial, we'll walk you through daily tips, code review strategies, and how to integrate these tools with Git, automated testing, and peer reviews, all while building and deploying a simple to-do list app. Let's dive in and see how AI can save you hours and reduce errors!

daily_tips.md

Using Cursor AI

→ AI Chat for Quick Help: Need a script for your CI pipeline? Ask AI Chat, "How do I write a script to run unit tests in GitHub Actions?" It's like having a coding buddy at your fingertips. For example, if you're setting up a new pipeline, ask, "Generate a script to cache npm dependencies in GitHub Actions," and get a ready-to-use snippet.
→ Generate Code from Comments: Type # Create a function to add two numbers, and Cursor AI will whip up the code. Perfect for those utility scripts in your pipeline, like a function to validate environment variables: # Create a function to validate environment variables for CI.
→ AI-Generated Commit Messages: Before committing, type # Generate commit message, and get a descriptive message based on changes, keeping your Git history clean. For instance, after updating a test, it might suggest, "Update unit tests for login function to include edge cases."

Using Windsurf AI

→ Cascade for Complex Tasks: Use Cascade for multi-file projects, like setting up CI/CD scripts across files. Describe, "Set up a CI/CD pipeline for this project, including building, testing, and deploying to Vercel," and let it handle the heavy lifting, generating files like github-actions.yml and vercel.json.
→ Supercomplete for Smart Suggestions: Get context-aware completions, speeding up writing pipeline-related code, like suggesting the correct syntax for a GitHub Actions step while you're typing.
→ Memories System for Continuity: Keeps your AI interactions flowing, perfect for long pipeline setup sessions. For example, ask, "How do I handle secrets in GitHub Actions?" then follow up, "Now, add that to my workflow," and it remembers the context.

code_review.md

AI isn't perfect, so let's ensure quality with detailed strategies:

→ Test with Sample Inputs: Run AI-generated functions with various inputs, including edge cases, to catch bugs early. For example, if AI generates a function to validate email addresses, test with "user@domain.com", "invalid.email", and empty strings to ensure it handles all cases.
→ Check for Errors: Ensure error handling is robust, especially for critical pipeline scripts. Look for unhandled exceptions, like in a deployment script where AI might miss error logging for failed builds.
→ Verify Standards: Make sure the code follows your project's rules, like using snake_case for variables or ensuring all functions have docstrings. For instance, check if a generated test case follows your team's naming convention, like test_add_user instead of TestAddUser.
→ Understand the Code: Read through; you'll maintain it later, so know how it works. For example, if AI generates a GitHub Actions workflow, understand each step, like actions/checkout@v3, to ensure it fits your needs.
→ Iterate and Refine: If it's off, refine your prompt or tweak the code. For example, if a test case misses an edge case, ask, "Add a test for negative numbers," and verify the updated output.

ci_cd_integration.md

Both tools are editor-based, so integration is indirect, but powerful. Here's how to make it work:

Git Integration

→ Cursor AI: Commit AI-generated code with AI-crafted messages, keeping your Git history informative. For example, after generating a new feature, commit with "# Generate commit message" to get "Add user authentication feature with JWT."
→ Windsurf AI: Write manual commit messages, but ask the AI for suggestions, like, "Suggest a commit message for adding CI tests," to get "Add CI tests for user authentication."

Setting Up CI/CD with GitHub Actions

→ Generate Workflow Files: In Cursor AI, ask, "Generate a GitHub Actions workflow for building and testing a Next.js app." Review and adjust the github-actions.yml file, like:

name: CI
on: [push, pull_request]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: 16
      - run: npm install
      - run: npm run build
      - run: npm test

→ Commit and push to Git, and GitHub Actions will run the pipeline.
→ Windsurf AI's Role: Use Cascade to generate multi-file scripts, like setting up tests across files. Describe, "Create a CI workflow for a React app with Jest tests," and review the generated files, ensuring they fit your pipeline.

automated_testing.md

→ Generate Unit Tests: Use Cursor AI to ask, "Generate unit tests for a function that adds two numbers," and get:

def add(a, b):
    return a + b

def test_add():
    assert add(2, 3) == 5
    assert add(-1, 1) == 0
    assert add(0, 0) == 0

→ Review and commit to run in your CI pipeline with PyTest.
→ Windsurf AI Example: Describe in Cascade, "Generate Jest tests for a React component that displays a to-do list," and get:

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

test('renders to-do list with items', () => {
  render();
  expect(screen.getByText('Buy milk')).toBeInTheDocument();
});

→ Review, commit, and run in CI with Jest.

deployment_scripts.md

→ Cursor AI: Ask, "Generate a deployment script for deploying to AWS Lambda," and get:

#!/bin/bash
aws lambda update-function-code --function-name my-function --zip-file fileb://function.zip
aws lambda publish-version --function-name my-function

→ Review for security, commit, and include in your CD pipeline.
→ Windsurf AI: Use Cascade to describe, "Create a Dockerfile for a Node.js app and a deployment script for Heroku," and get:

FROM node:16
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
CMD ["npm", "start"]

And a script:

#!/bin/bash
heroku container:push web --app my-app
heroku container:release web --app my-app

→ Review, commit, and deploy via your CD pipeline.

peer_reviews.md

→ Pre-Check with AI: Before committing, use AI Chat to check, "Are there any security issues in this deployment script?" to catch vulnerabilities like hardcoded secrets.
→ Explain During Reviews: Use AI to explain code to peers, like "Explain how this GitHub Actions workflow handles build failures," making reviews smoother.
→ Mark AI-Generated Code: Add comments like # Generated by AI for transparency, ensuring reviewers know to scrutinize it, especially for pipeline scripts.

todo_app_example.md

Let's build a Next.js to-do list app and deploy it using CI/CD, with more detailed steps:

Generate the App with Cursor AI:

→ Type # Create a basic Next.js app structure. Review and implement to-do list features, like:

// pages/index.js
import { useState } from 'react';

export default function Home() {
  const [todos, setTodos] = useState([]);
  const addTodo = (text) => setTodos([...todos, text]);
  return (
    <div>
      <input onChange={(e) => addTodo(e.target.value)} />
      <ul>{todos.map((todo, i) => <li key={i}>{todo}</li>)}</ul>
    </div>
  );
}

→ Use AI Chat to refine, asking, "Add a delete button for each to-do item."

Write Tests with Both Tools:

→ In Cursor AI, ask, "Generate unit tests for the add function," and get:

import { render, screen, fireEvent } from '@testing-library/react';
import Home from './pages/index';

test('adds a to-do item', () => {
  render(<Home />);
  const input = screen.getByRole('textbox');
  fireEvent.change(input, { target: { value: 'Buy milk' } });
  expect(screen.getByText('Buy milk')).toBeInTheDocument();
});

→ In Windsurf AI, use Cascade to describe, "Create Jest tests for the to-do list component," and review the output, ensuring coverage.

Set Up GitHub Actions with AI:

→ Ask Cursor AI, "Generate a GitHub Actions workflow for building and testing a Next.js app," and get:

name: CI
on: [push, pull_request]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: 16
      - run: npm install
      - run: npm run build
      - run: npm test

→ Commit and push to Git, watching the pipeline run.

Generate Deployment Scripts:
- → Use Cursor AI to ask, "Generate a deployment script for Vercel," and get:
```
#!/bin/bash
vercel --prod
```
- → Review, commit, and include in your CD pipeline.
Verify and Iterate: Access your live app, test the to-do list, and refine with AI if needed, like fixing a bug with AI Chat, "Fix the to-do list to handle empty inputs."

conclusion.md

AI can save hours by generating test cases and scripts, reducing errors in peer reviews and automated testing. It's like having a coding assistant that preps your pipeline for success, making deployments smoother and more reliable.

resources.md