Jira Issue Resolution Time Predictor

This project analyzes and predicts resolution times for Jira issues using machine learning techniques.

Features

• Retrieves issue data from MongoDB database
• Categorizes resolution times into meaningful buckets
• Analyzes resolution patterns by issue attributes (components, labels, priority, etc.)
• Creates visualizations to understand resolution time distributions
• Implements multiple prediction models:
  • Text-based models (TF-IDF and BERT)
  • Topic models
  • Distribution-based predictions
  • Stacked machine learning approach

Installation

1. Clone this repository
2. Install dependencies: pip install -r requirements.txt
3. Optional: For BERT and topic models, install additional dependencies:

   pip install sentence-transformers bertopic
   

Usage

python main.py --project PROJECTNAME --mongo-uri "mongodb://user:password@host:port/"

Configuration

Edit config.py to customize:

• Output directory
• Resolution time categories
• Model parameters
• Test/train split ratio

Output

The tool generates:

• Visualizations of resolution time distributions
• Heatmaps showing resolution patterns by different attributes
• Trained ML models for future predictions
• Analysis reports with accuracy metrics
• Comparison of different prediction approaches