Jira Sprint Analysis Tool

A Python-based tool for analyzing Jira sprint data, comparing original estimates against AI-generated estimates and actual time spent across different disciplines (BA, BE, FE, TA, QA).

Features

• Interactive web interface using Streamlit
• Processes Jira export data to analyze estimation accuracy
• Compares original estimates, AI-generated estimates, and actual time spent
• Breaks down analysis by discipline (BA, BE, FE, TA, QA)
• Calculates estimation improvements:
  • Original vs Actual deviation percentage
  • AI vs Actual deviation percentage
  • Overall estimation improvement percentage
• Generates detailed Excel report with:
  • Summary Dashboard
  • Analysis Results
  • Data Visualizations
• Creates interactive visualizations for estimate comparisons
• Handles missing data gracefully with detailed reporting

Quick Start

1. Clone the repository:

git clone <repository-url>
cd halfords-jira-sprint-analysis

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the Streamlit app:

streamlit run app.py

5. Upload your Jira export Excel file through the web interface or place it in the input directory.

Project Structure

halfords-jira-sprint-analysis/
├── jira_analysis/           # Main package directory
│   ├── __init__.py         # Package initialization
│   ├── analyzer.py         # Core analysis functionality
│   ├── visualization.py    # Plotting and Excel formatting
│   └── utils.py           # Utility functions
├── tests/                  # Test directory
│   ├── __init__.py
│   ├── test_analyzer.py
│   ├── test_visualization.py
│   └── test_utils.py
├── input/                  # Place Jira export files here
├── output/                # Generated reports appear here
├── app.py                 # Streamlit web application
├── requirements.txt       # Production dependencies
├── requirements-dev.txt   # Development dependencies
├── setup.py              # Package setup file
├── pyproject.toml        # Build system requirements
├── tox.ini              # Tox configuration
└── README.md

Development

1. Install development dependencies:

pip install -r requirements-dev.txt

2. Install the package in development mode:

pip install -e .

3. Run tests:

python -m pytest

Input File Format

The tool expects a Jira export Excel file with the following columns:

• Key: Jira ticket key
• For each discipline (QA, TA, FE, BA, BE):
  • Original Estimate based on old way
  • Revised Estimate based on AI
  • Actual Time based on AI

Output

The tool generates:

1. Interactive web interface with:
   • Data preview
   • Analysis results
   • Interactive visualizations
2. Excel report containing:
   • Summary statistics
   • Detailed analysis by discipline
   • Visualizations
   • Data quality metrics

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.