🚀 Acaris Backend System with CI/CD Pipeline

📌 Overview

Acaris is a backend system designed to manage academic consultation processes between students and lecturers. This project implements a cloud-native microservices architecture integrated with a CI/CD pipeline to improve system scalability, reliability, and deployment efficiency.

The system is deployed using Google Cloud Run and utilizes Docker containerization with automated workflows via GitHub Actions.

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🎯 Objectives

• Implement a backend system using microservices architecture
• Automate build, testing, and deployment using CI/CD
• Evaluate system performance using DORA metrics
• Ensure system reliability through comprehensive testing

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🏗️ System Architecture

Architecture Style:

• Microservices
• REST API
• Cloud-native deployment

Main Services:

• Auth Service → Authentication & authorization
• Acaris Service → Core academic features
• Chatbot Service → Automated assistance

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⚙️ Tech Stack

Backend

• Node.js
• Express.js

DevOps

• Docker
• GitHub Actions (CI/CD)

Cloud

• Google Cloud Run

Database

• (Relational / NoSQL depending on implementation)

Authentication

• Firebase Authentication

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📁 Project Structure

/services
  /auth-service
  /acaris-service
  /chatbot-service
/docker
/.github/workflows

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🔄 CI/CD Pipeline

The CI/CD pipeline is implemented using GitHub Actions and includes:

1. Code Push (GitHub)
2. Build Process
3. Automated Testing
4. Docker Image Build
5. Push to Container Registry
6. Deploy to Google Cloud Run

Benefits:

• Faster deployment cycles
• Reduced manual errors
• Continuous integration and delivery

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🐳 Containerization

Each service is containerized using Docker:

docker build -t service-name .
docker run -p 3000:3000 service-name

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☁️ Deployment

Deployment is handled via Google Cloud Run:

GitHub → GitHub Actions → Docker Image → Cloud Run

Features:

• Scalable
• Serverless
• Fully managed

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🔌 API Endpoints

Authentication

POST /auth/register
POST /auth/login

Core Features

GET /schedules
POST /booking
GET /documents

Chatbot

POST /chatbot

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🧪 Testing Strategy

1. Functional Testing

• Validate all API endpoints
• Ensure correct response and status codes

2. Integration Testing

• Validate interaction between services

3. Regression Testing

• Ensure existing features remain stable after updates

4. Performance Testing

• Tool: Apache JMeter

• Scenarios:

  • 50 users
  • 100 users

• Metrics:

  • Response Time
  • Throughput
  • Error Rate

5. CI/CD Pipeline Testing

• Pipeline duration
• Success/failure rate
• Deployment frequency

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📊 Performance Evaluation (DORA Metrics)

Deployment Frequency

$$ Deployment\ Frequency = \frac{|D|}{t_n - t_0} $$

Lead Time for Changes

$$ Lead\ Time = \frac{1}{n} \sum (t_{deploy} - t_{commit}) $$

Change Failure Rate

$$ CFR = \frac{D_f}{D} \times 100% $$

Mean Time to Recovery (MTTR)

$$ MTTR = \frac{1}{n} \sum (t_{recovery} - t_{failure}) $$

Pipeline Success Rate

$$ Success\ Rate = \frac{D_s}{D} \times 100% $$

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✅ Expected Outcomes

• Fully functional backend system
• Automated CI/CD pipeline
• Cloud-deployed microservices
• Comprehensive testing results
• Performance evaluation using DORA metrics

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📌 Development Workflow

1. Local Development
2. API Testing (Postman)
3. Dockerization
4. Manual Deployment
5. CI/CD Implementation
6. Automated Testing
7. Data Collection & Evaluation

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👨‍💻 Author

• Name: M. Arifin Syam
• Program: Informatics Engineering
• Project: Thesis Implementation (CI/CD Microservices System)

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📄 License

This project is developed for academic purposes.