Bug-Tracking-And-Reporting-system

This project presents an AI-enabled web application designed to identify, classify, and track software bugs efficiently. By integrating Natural Language Processing (NLP) for automated bug classification, a centralized dashboard for monitoring, and role-based user access, the system significantly enhances debugging efficiency, accuracy, and team collaboration.

System Strengths & Key Contributions

1. Intelligent Automation

Implements NLP-based severity and category prediction to minimize manual analysis and ensure faster prioritization.

2. Enhanced Collaboration

Provides a centralized dashboard with real-time updates, enabling seamless coordination among testers, developers, and project managers.

3. Scalable Architecture

Modular system design supports easy integration of additional features, new datasets, or extended AI capabilities.

4. Data-Driven Insights

Includes visual analytics and performance metrics to help teams identify patterns, track progress, and make informed decisions.

Features

1. AI-Powered Classification: Automatically predicts bug severity and category using NLP techniques.

2. Bug Tracking Dashboard: Displays open, in-progress, and resolved issues in a structured format.

3. Automated Assignment: Assigns bugs to the appropriate developer based on category and severity predictions.

4. Visualization Tools: Offers real-time charts and analytical insights on project performance.

5. Notifications: Sends automated updates for bug assignments, changes, and resolutions.

6. Secure Access: Provides role-based authentication for testers, developers, and managers.

Screenshots

Dashboard Page

Report Bug Page

Advantages

1. Automation: Minimizes manual effort in categorizing and prioritizing bugs.

2. Accuracy: Ensures consistent NLP-based severity prediction.

3. Transparency: Real-time monitoring improves coordination and workflow clarity.

Dataset

The dataset used for AI classification consists of structured bug reports containing:

Titles

Descriptions

Severity levels

Categories

User roles

Attachments

How to Run

1. Clone the Repository

git clone <https://github.com/shanmugaharini21/Bug-Tracking-And-Reporting-system/tree/main>

Acknowledgements

This project was developed using React.js and Node.js, which provided a strong full-stack foundation, along with Python NLP libraries that enabled intelligent bug classification. MySQL supported reliable data storage, and Chart.js contributed to clear visual analytics. We also acknowledge the research community for their valuable work in AI-driven issue tracking and natural language processing, which guided and strengthened this system.