Cloud QA Strategy Project Overview

Context

This project documents a comprehensive QA strategy tailored for a scalable web application deployed on Amazon Web Services (AWS). It was designed to demonstrate my ability to evaluate and address quality assurance concerns specific to cloud-native applications, especially in areas such as scalability, security, and CI/CD integration.

Problem

Testing cloud applications introduces challenges beyond those of traditional software due to dynamic infrastructure, distributed systems, scalability, and service integrations. Manual processes and inconsistent environments can lead to performance issues, security vulnerabilities, and slower deployments.

Solution

To address these challenges, a QA strategy was created to focused on:

• Test Types Covered: Functional, performance, scalability, and security testing strategies.
• Cloud Environment Setup: Infrastructure configuration using AWS services such as EC2, S3, RDS, and ALB.
• Data Management: Secure test data generation, masking, and rollback strategies.
• Continuous Testing Integration: Automation of testing pipelines using GitHub Actions and Jenkins, integrated with AWS services.
• Tools & Process: Documentation was authored using Markdown in VS Code and version controlled in GitHub.

Impact

This QA strategy helped simulate a real-world cloud testing framework while demonstrating best practices in version control, automation, and environment replication. The comprehensive QA strategy provides actionable guidance for development teams while maintaining clarity and logical organization throughout.

Key Takeaways

Through this project I developed stronger skills in:

• Writing detailed and technical documentation
• Structuring information for clarity and reuse
• Understanding cloud-native testing constraints and workflows
• Automating quality assurance using cloud-based CI/CD tools

CI/CD Test Automation Example (GitHub Actions)

This YAML configuration sets up a GitHub Actions workflow that automatically runs a suite of Selenium tests whenever code is pushed or a pull request is opened. Automating test execution at this stage ensures that new changes are validated early in the development process, reducing the risk of bugs reaching production.

Here’s what each section of the workflow does:

This workflow is based on a standard GitHub Actions template I studied during the project. While I didn’t write it entirely from scratch, I modified it to suit the project’s test structure and repository setup.

name: Cloud QA Tests

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Run Selenium tests
        run: pytest tests/ui/

• on: [push, pull_request] ensures that the workflow triggers on every new commit or pull request to the main branch.
• runs-on: ubuntu-latest spins up a new virtual machine in GitHub’s cloud to execute the tests.
• checkout@v3 pulls the current version of the code from the repository.
• The pytest command runs automated Selenium test scripts located in the tests/ui/ directory.

By including this file, testing becomes a seamless part of the development cycle, catching issues before they escalate and contributing to the application’s long-term security and stability.

Reflection

This project bridged my interest in QA testing with my growing technical writing skills and cloud understanding. It gave me the opportunity to create documentation that mirrors how real teams plan, communicate, and test in a DevOps-driven cloud environment. It also reinforced my skills in various cloud domains such as infrastructure as code, test automation, and secure cloud architecture.

Project Link

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Cloud QA Strategy Documentation