AI Project - Software Effort Estimation

This project provides tools for software effort estimation using various models including COCOMO II and machine learning approaches.

Project Structure

The project is organized as follows:

• app/: Main application code

  • Flask/FastAPI web application
  • API routes and endpoints

• src/: Source code

  • api/: API endpoints
  • models/: ML models
    • cocomo/: COCOMO models
    • ml_models/: Machine learning models
    • multi_model/: Multi-model integration
  • analyzer/: Requirement analyzer
  • feedback/: Feedback system
  • utils/: Utility functions
  • data_processing/: Data processing scripts

• docs/: Documentation

  • API documentation, user guides, research papers
  • Detailed guides for features and deployment

• scripts/: Shell scripts (.sh files)

  • Deployment, testing, and management scripts
  • See scripts/README.md for details

• docker/: Docker configuration

  • Dockerfiles and docker-compose files
  • Nginx configuration
  • See docker/README.md for details

• tools/: Utility Python scripts

  • Demo scripts, testing tools, model trainers
  • See tools/README.md for details

• config/: Configuration files

  • Requirements files, CSV data, patches
  • See config/README.md for details

• notebooks/: Jupyter notebooks for analysis and demonstrations

• tests/: Test files

• data/: Data files

  • raw/: Raw data
  • processed/: Processed data

• static/: Static resources (CSS, JS, images)

Getting Started

To get started with the project:

1. Clone the repository:

git clone https://github.com/Huy-VNNIC/AI-Project.git
cd AI-Project

2. Set up the environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Run the requirement analyzer API:

source venv/bin/activate

API Documentation

The API will be available at http://localhost:8000, and the documentation can be accessed at http://localhost:8000/docs.

License

Copyright (c) 2025 Huy-VNNIC