⚡ CODESTORM - From Jira Ticket to Deployed Code in One Command

🏆 Built in 12 Hours - Our First Hackathon Masterpiece 🏆

Hackathon Problem Statement

Revolutionize the software development lifecycle by creating an AI-powered assistant that automates workflows from Jira task to production-ready code---seamlessly integrating planning, coding, testing, and deployment. Your goal: Build an intelligent system within Void Editor that eliminates manual toil, reduces context-switching, and accelerates delivery---while maintaining high standards of quality.

The Challenge: Autonomous Jira-to-Code Pipeline

Our solution creates a complete end-to-end automation system that transforms Jira tickets into production-ready code with minimal human intervention.

12-Hours Hackathon!

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🎯 What We Built

An intelligent automation pipeline that:

• Fetches Jira tickets automatically using OAuth Atlassian integration
• Generates complete, production-ready code using OpenAI LLM
• Updates Jira ticket status in real-time
• Tests and iterates code based on user feedback
• Automatically pushes to GitHub
• Manages project lifecycle from "To Do" to "Done"

🔧 Prerequisites

• Python 3.7+ installed
• OpenAI API Key
• Jira Base URL
• Jira Email Address
• Jira API Token
• GitHub credentials (for deployment)

📦 Installation

bash

$ pip install openai requests python-dotenv

⚙️ Setup

1. Create a .env file in your project root:

env

OPENAI_API_KEY=your_openai_api_key
JIRA_BASE_URL=https://your-domain.atlassian.net
JIRA_EMAIL=your-email@domain.com
JIRA_API_TOKEN=your_jira_api_token

1. Ensure your Jira account has proper permissions
2. Set up GitHub authentication for automatic deployment

🏃‍♂️ How to Run

bash

$ python main.py

📋 Workflow Process

1. Ticket Fetching

• Connects to Jira using OAuth 2.0 authentication
• Fetches all available tickets from your Jira workspace
• Saves ticket details as prompt files in /prompts folder

2. Code Generation

• User selects specific Jira ticket by entering issue key
• System reads ticket requirements from saved prompt file
• OpenAI LLM generates complete project structure and code
• Automatically transitions Jira ticket from "To Do" to "In Progress"

3. Testing & Iteration

• Generated code is automatically tested
• User gets option to keep code or enhance it
• System iterates in a loop until user confirms satisfaction
• Continuous improvement through AI feedback loop

4. Deployment

• User gets option to push code to GitHub
• For personal repositories: Jira ticket automatically moves to "Done"
• For organization/open source repos: Ticket updates after code merge
• Zero manual intervention required

💻 Sample Execution

bash

brije@Brijesh MINGW64 ~/Desktop/Eagles x hackathon
$ python main.py
🔁 Loading environment variables...
✅ Env Check:
  OPENAI_API_KEY exists? Yes
  JIRA_BASE_URL: https://yorkhackathonteam1.atlassian.net
  JIRA_EMAIL: yorkhackathonteam1@gmail.com
  JIRA_API_TOKEN exists? Yes
📡 Fetching all Jira issues for prompts...
✅ Saved 5 prompt files to /prompts
✅ Prompt files saved in /prompts/
🔑 Enter Jira Issue Key to generate code (e.g., PROJ-123): WEAT-4
📡 Fetching issue: WEAT-4
🧠 Generating code...
🔄 Jira issue WEAT-4 transitioned to 'In Progress'.
💾 Writing files to 'generated_project/'...
✅ Created: generated_project\package.json
✅ Created: generated_project\vite.config.ts
✅ Created: generated_project\tsconfig.json
✅ Created: generated_project\tailwind.config.js
✅ Created: generated_project\postcss.config.js
✅ Created: generated_project\public/index.html
✅ Created: generated_project\src/main.tsx
✅ Created: generated_project\src/App.tsx
✅ Created: generated_project\src/pages/HomePage.tsx
✅ Created: generated_project\src/components/custom/WeatherDisplay.tsx
✅ Created: generated_project\src/hooks/useWeatherStore.ts
📁 Project written to: generated_project
✅ Done! Check the 'generated_project' folder.

🌟 Key Features

• 🔄 Automated Workflow: Complete automation from Jira ticket to deployed code
• 🤖 AI-Powered: Leverages OpenAI LLM for intelligent code generation
• 📊 Real-time Updates: Synchronizes Jira ticket status automatically
• 🔧 Iterative Enhancement: Continuous code improvement through AI feedback
• 🚀 One-Click Deployment: Direct GitHub integration for seamless deployment
• 📁 Organized Output: Structured project generation with proper file hierarchy

🛠️ Technical Stack

• Backend: Python
• AI/ML: OpenAI GPT API
• Project Management: Jira REST API
• Version Control: GitHub API
• Authentication: OAuth 2.0 (Atlassian)
• Environment Management: python-dotenv

👥 Team

First-time hackathon participants who transformed an ambitious idea into a working solution in record time!

🎉 Impact

This solution eliminates:

• Manual context-switching between tools
• Time-consuming code setup and boilerplate generation
• Manual Jira ticket status updates
• Deployment pipeline configuration
• Code review bottlenecks for standard implementations

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"Every expert was once a beginner. Every pro was once an amateur." 🔥