How GitHub Actions and GitLab CI/CD Accelerate Software Delivery

In current software development, speed and reliability are not optional. Companies need to constantly release updates, fix bugs quickly, and ensure everything works correctly. Traditionally, this process involved many manual tasks prone to human error.

Nowadays, tools like GitHub Actions and GitLab CI/CD have standardized a methodology called CI/CD (Continuous Integration and Continuous Delivery).

This blog explains what these tools are, how they really work, and why they are essential for an agile methodology.

What is a CI/CD Pipeline?

A pipeline is an automated structure that moves code from the developer's machine to the production environment. This process eliminates human intervention in repetitive tasks, ensuring that every change in the software is:

1. Verified: Automatic tests are run.
2. Packaged: The application is compiled.
3. Deployed: It is sent to the final servers.

To understand the tools, we must first define the process they automate. CI/CD stands for two concepts that happen one after the other:

1. Continuous Integration (CI): It is the practice where developers save and merge their code into a central repository several times a day. Each time the code is saved, an automatic system runs tests to verify that the new changes do not cause conflicts or errors in what was already done.
2. Continuous Delivery (CD): It is the next step. Once the code has passed the integration tests, the system automatically prepares it to be released to production (the environment users see). The goal is for the software to always be ready to be deployed at any time.

The Role of GitHub Actions and GitLab CI/CD

Both GitHub and GitLab are platforms where source code is stored and managed. GitHub Actions and GitLab CI/CD are the automation engines integrated into these platforms.

Their function is simple: execute a predefined list of tasks every time a specific event occurs. For example, when someone saves a file, these tools read instructions (written in configuration files) and execute step by step what the engineering team has defined.

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How does the workflow work step by step?

Instead of relying on a person to remember to run commands, the flow looks like this:

1. The Trigger It all starts when a developer completes a task and pushes their code to the cloud repository (GitHub or GitLab).

2. Automatic Testing Immediately, the tool (GitHub Actions or GitLab CI/CD) starts a temporary server and downloads the project. There it runs a series of automatic tests:

• Verifies that the code logic is correct.
• Checks basic security standards.
• Ensures that new features have not broken old functions.

If any test fails, the process stops and notifies the team exactly where the error is.

3. Build If the tests pass, the tool "builds" the application. This means it takes all the code files and packages them into a final executable format, ready to be installed or uploaded to a server.

4. Deployment Finally, the tool takes that verified package and sends it to the company's servers. Within minutes, the update is available to end users without anyone having to manually touch the servers.

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1. Automation with GitHub Actions

GitHub uses an event-based approach. Its workflows are defined in .yaml files and allow great modularity.

Example implementation: The following snippet shows a configuration for an application that must be tested every time code is pushed.

 
# File: .github/workflows/main.yml

name: Production CI/CD
on:
  push:
    branches: [ "main" ] # Trigger: Only activates on the main branch

jobs:
  build-and-test:
    runs-on: ubuntu-latest # Virtualized execution environment
    
    steps:
    - name: Checkout code
      uses: actions/checkout@v3
      
    - name: Setup environment (Node.js)
      uses: actions/setup-node@v3
      with:
        node-version: 18
        
    - name: Run Unit Tests
      run: npm test # Quality validation
      
    - name: Deploy to Server
      if: success() # Conditional: Only runs if tests passed
      run: ./scripts/deploy.sh

Flow analysis:

• Trigger (on): The system monitors the main branch. Any change there starts the process.
• Jobs: Defines the tasks. In this case, the system provisions a Linux server (ubuntu-latest), downloads the code, and runs the tests.
• Security: The instruction "if: success()" is critical. It ensures that deployment never occurs if the previous quality validation has failed.

Here we have an example of a pipeline where the trigger was successfully activated by making a change; the pipeline ran and it is shown that the Unit Tests and Build were successful and it was deployed to Docker Hub.

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2. Automation with GitLab CI/CD

GitLab offers an integrated architecture based on "Stages." It is ideal for organizations seeking a unified view of the entire DevOps cycle.

Example implementation: In GitLab, the .gitlab-ci.yml file defines a strict execution sequence.

 
# File: .gitlab-ci.yml

stages:          
  - build
  - test
  - deploy

compilation:
  stage: build
  script:
    - echo "Starting binary compilation..."
    - npm install
    - npm run build

validation:
  stage: test
  script:
    - npm run test-security
    - npm run test-unit

production:
  stage: deploy
  script:
    - echo "Deploying stable version..."
    - ./deploy-to-cloud.sh
  only:
    - main # Environment restriction