🧠 AI-Powered Ticket Routing & SLA Breach Prediction in JIRA

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🔍 Project Overview

Managing technical support efficiently in JIRA can be challenging with manual triage and SLA tracking. This project introduces an AI-powered automation layer that:

• Auto-routes tickets based on predicted category and severity
• Predicts SLA breaches before they occur
• Prioritizes high-risk tickets using rule-based and ML logic
• Helps teams stay ahead with proactive support operations

🎯 Goal: Reduce support delays, improve ticket handling efficiency, and increase SLA adherence through intelligent automation.

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🛠️ Features

• ✅ AI-driven ticket classification based on description & metadata
• ⏰ Real-time SLA breach prediction based on response patterns
• 📈 Dashboard-ready data export for team insights
• ⚙️ Modular Python scripts for plug-and-play usage with JIRA REST API
• 📥 Works seamlessly with Jira Service Desk projects

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📚 Published Research

📄 Title: Optimizing Jira-Based Support Operations With AI
🗞️ Journal: IJARIIT – International Journal of Advance Research, Ideas and Innovations in Technology
🔗 Read Full Paper

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✍️ Blog & Article Coverage

📘 Dev.to Post
📝 AI-Powered Ticket Routing & SLA Prediction in JIRA – My Real-World Automation Journey

📘 Medium Article
📝 AI-Powered JIRA Ticket Routing & SLA Breach Prediction with Python

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📁 Project Structure

├── /data/                → Sample datasets & JIRA export files
├── /models/              → Pre-trained classification & prediction models
├── /notebooks/           → Jupyter notebooks for training & evaluation
├── /scripts/             → Python scripts to trigger classification/prediction
├── /api/                 → Flask-based RESTful API for automation
├── /screenshots/         → Sample outputs and workflow screenshots
└── README.md             → Project documentation

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📁 Folder Structure

├── api/                       # Flask app with endpoints
├── automation-rules/          # JSON rules for JIRA
├── dummy-data/                # Sample ticket datasets
├── screenshots/               # Visuals of workflows and dashboards
├── README.md                  # This file
└── requirements.txt           # Python dependencies

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🚀 Getting Started

1. Clone the repo

git clone https://github.com/your-username/jira-ai-sla-automation.git
cd jira-ai-sla-automation

2. Create virtual environment

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

3. Install dependencies

pip install -r requirements.txt

4. Run the Flask server

python api/app.py

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📊 Workflow Overview

🔁 Automation Flowchart

🧠 SLA Dashboard Preview

⚙️ Sample Automation Workflow

🚀 Ticket Routing Visual

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📚 Use Cases

This solution is ideal for:
• IT Support Teams managing SLA-heavy environments
• Product Support Units handling large ticket volumes
• DevOps teams seeking intelligent triage and automation
• Startups and Enterprises using Atlassian JIRA for support workflows

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🧠 Tech Stack

• Python: Core scripting and model orchestration
• Scikit-learn / XGBoost: Model training and tuning
• NLTK / spaCy: Text preprocessing and tokenization
• Flask: Lightweight REST API for integration
• Pandas / Matplotlib / Seaborn: Reporting and analytics
• JIRA REST API: For ticket access and updates

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📌 Author

Arooj Javed
Support Engineer | Workflow Automator | Python + JIRA Enthusiast
GitHub: @aroojjaved93

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📄 License

This project is licensed under the MIT License.

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🌍 Contributions & Feedback

Stars, forks, and contributions are highly welcome!
Feel free to create issues or pull requests to suggest improvements.