Here’s the updated README with the backend repository link included:

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🚀 AI-Powered Test Automation Agent

📌 Project Overview

Modern software testing is time-consuming, costly, and often inefficient. Our AI-powered Test Automation Agent streamlines the testing process by analyzing Figma designs, generating structured test cases, automating script execution, and integrating seamlessly with CI/CD pipelines. This eliminates manual effort and accelerates software deployment.

🎯 Key Features

• AI-Driven Test Case Generation: Extracts functional and UI test cases from Figma designs.
• Automated Web Scraping: Retrieves relevant HTML element IDs and classes.
• Selenium-Based Test Execution: AI-generated scripts are executed automatically.
• Seamless CI/CD Integration: Ensures continuous testing within the development pipeline.
• Optimized Performance: Multi-threading and API load balancing for faster execution.

🛠 Technology Stack

• Backend: Python, Node.js
• Frontend: React.js
• Automation Framework: Selenium, Shell Scripting
• Database: MongoDB
• CI/CD: GitLab
• AI Model: Google Gemini API

🚧 Challenges & Solutions

Challenges:

• Selenium scripts are not universally adaptable to all websites.
• Error detection in system testing varies across different web applications.
• API rate limits slow down test case generation.
• Test case accuracy needs continuous improvement.

Solutions:

• Optimized AI models to improve test case generation accuracy.
• Implemented web scraping techniques to fetch relevant UI elements dynamically.
• Multi-threading & multiple API keys to handle API rate limits effectively.
• Adaptive error detection mechanisms for robust testing on different websites.

🔄 Workflow

1. Extract Requirements: Analyze Figma designs to determine UI-based test scenarios.
2. Generate Test Cases: AI converts requirements into structured JSON test cases.
3. Web Scraping: Fetch relevant HTML element IDs and classes for execution.
4. AI-Powered Script Generation: AI generates Selenium scripts for test execution.
5. Automated Execution: The scripts run within a CI/CD pipeline for continuous testing.
6. Result Logging: The output is stored in a structured JSON format.

📌 CI/CD Integration

• The entire process runs within GitLab CI/CD pipelines using YAML configuration files.
• Automated triggers ensure testing upon each commit or pull request.
• Results are stored for future reference and debugging.

📈 Impact

✅ Instant Code Testing – Developers can validate code instantly.
✅ Faster Deployments – Reduces the time required to push changes to production.
✅ Reduced Testing Costs – Eliminates the need for manual testers.
✅ Improved Accuracy – AI-generated test cases enhance reliability.

📂 Installation & Setup

1. Clone the repository:

   git clone https://github.com/your-repo-name.git  
   cd your-repo-name  

2. Install dependencies:

   pip install -r requirements.txt  # For Python  
   npm install  # For Node.js frontend  

3. Run the Backend:

   python app.py  

4. Start the Frontend:

   npm start  

5. Run Tests:

   pytest tests/  

🔗 Backend Repository

The backend source code is available at:
🔗 AI-Powered Test Automation Backend

📜 License

This project is licensed under the MIT License.