BugChetana

A role-based bug tracking web application with AI-assisted severity prediction.

Live Demo

• Production URL:📍 https://bug-chetana-ai.vercel.app
• Note: The frontend is deployed on Vercel, and the backend is deployed on Railway.

Features

• Bug lifecycle workflow: Track bugs from initial submission through testing and final verification so nothing slips through the cracks.
• Role-based access: Ensure users only see and interact with what is relevant to their job, whether they are fixing code, testing features, or managing releases.
• AI-assisted bug review: Receive automated, sometimes sarcastic feedback (Roast Mode) on bug submissions to encourage better reporting habits.
• Severity prediction: Automatically estimate how critical a bug is right when it's reported, helping teams prioritize their workload efficiently.
• Bug history timeline: Keep a clear record of when a bug was reported, tested, and resolved for full accountability.

Roles & who does what

• Developer: Can submit new bug reports, view severity predictions, receive AI feedback on their submissions, and monitor their personalized dashboard.
• QA (Quality Assurance): Responsible for managing bug reports across a project, submitting test results (Pass, Fail, Blocked, Verified, Reassign), and keeping track of the project's overall bug lists.
• Release Manager: Holds administrative control to manage projects, assign user roles, group bugs into targeted releases, and monitor the overall health of those releases.

Getting started (for developers)

Prerequisites

• Docker and Docker Compose installed on your machine.
• Git.

Setup Steps

1. Clone the repository:

   git clone https://github.com/Anujakhatri/BugChetana.git
   cd BugChetana

2. Configure Environment Variables: Copy the example environment variables file in the root directory and fill in your keys.

   cp .env.example .env

   You will need to provide values for variables like SECRET_KEY and GROQ_API_KEY. Reference .env.example for the complete list of required keys. Never commit your actual secrets.

3. Run with Docker Compose: Start the database, backend API, and frontend development server all at once using Docker Compose:

   docker compose up --build

4. Access the Application locally:

   • Frontend: http://localhost:5173
   • Backend API: http://localhost:8000
   • Database (PostgreSQL): Exposed on localhost port 5433

Tech stack

• Frontend: React 19, Vite, TailwindCSS 4, React Router DOM, Axios
• Backend: Python, Django 6, Django REST Framework
• Database: PostgreSQL 15
• AI / ML: XGBoost (Severity Prediction), Groq API (AI Review/Roast Mode)
• Infrastructure: Docker & Docker Compose

Screenshots

Developer Dashboard

QA Dashboard

Release Manager Dashboard

License

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

Contact

Anuja Khatri — GitHub