BugPriority AI: Enterprise Bug Intelligence OS

Overview

BugPriority AI is a full-stack, AI-driven platform designed to automate software defect triage. By leveraging a Random Forest (RF) classifier and a Retrieval-Augmented Generation (RAG) pipeline, the system predicts bug severity with 95% accuracy against a dataset of 221,000+ real-world records.

---

🚀 Key Features

• Predictive Severity Analysis: Supervised learning via Random Forest to categorize bugs (S1-S4) instantly.
• RAG-Powered Explainability: Semantic search using ChromaDB to find and display similar historical defects for every submission.
• Enterprise Security: JWT-based authentication with a secure handshake protocol and multi-tenant data isolation.
• Bulk Data Ingestion: Dynamic CSV/JSON upload logic for training models on new company-specific data.
• Interactive Dashboard: Real-time analytics and bug exploration powered by a high-performance PostgreSQL backend.

---

🛠️ Tech Stack

• Frontend: React.js, Tailwind CSS, Axios
• Backend: FastAPI (Python 3.11+), SQLAlchemy
• Machine Learning: Scikit-learn (Random Forest), Sentence-Transformers
• Databases: * PostgreSQL: Relational storage for bug metadata.
  • ChromaDB: Vector/Semantic Search for RAG.
  • Supabase: Cloud-hosted production database (Deployment Phase).
• Data Source: Official Mozilla Bugbug Dataset

---

📦 Installation & Setup

1. Prerequisites

• Python 3.11+ (Optimized for FastAPI & ML performance)
• PostgreSQL 14+ (Local Dev) or Supabase Account (Production)
• Node.js & npm (Frontend)

2. Data Acquisition

Download the universal training dataset (~221k records):

pip install bugbug
python3 -c "from bugbug import bugzilla, db; db.download(bugzilla.BUGS_DB)"

3. Backend Configuration

1. Navigate to the /backend directory.
2. Create a .env file and add your credentials:

DATABASE_URL="postgresql://USERNAME:PASSWORD@localhost:5432/bugbug_data"
SECRET_KEY="your_jwt_secret_key"

4. Install dependencies and initialize the system:

pip install -r requirements.txt
python3 make_data.py        # Ingest records to SQL
python3 Train_Universal.py   # Train the RF Model
uvicorn main:app --reload    # Start the API

Frontend Setup

Navigate to the /frontend directory.

Install dependencies and launch the development server:

npm install
npm start