https://github.com/deepvamja/bug_vision_ai

https://github.com/deepvamja/bug_vision_ai

Last synced: 28 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/deepvamja/bug_vision_ai
• Owner: Deepvamja
• Created: 2025-12-26T13:08:30.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-12-26T15:25:10.000Z (6 months ago)
• Last Synced: 2025-12-28T02:24:13.948Z (6 months ago)
• Language: Python
• Size: 12.9 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.txt

Awesome Lists containing this project

README

          
🐞 BugVision AI

Intelligent Bug Screenshot Classification & Explainability System

BugVision AI is a production-style deep learning system that automatically classifies software bug screenshots into real-world error categories and explains why the model made each decision using visual heatmaps.

🚀 The Problem (Real & Industrial):

In modern software teams:

QA engineers report bugs using screenshots

Developers manually read and triage bugs

Large teams receive hundreds of screenshots daily

Bug categorization becomes slow, error-prone, and inconsistent

❌ Manual triaging

❌ Delayed debugging

❌ No visibility into recurring bug patterns

BugVision AI solves this by automating bug classification using computer vision.

💡 The Solution

🧠 What BugVision AI Does

✔️ Accepts bug screenshots as input

✔️ Classifies them into 6 real-world error types

✔️ Generates confidence scores

✔️ Explains predictions using Grad-CAM heatmaps

✔️ Stores prediction history

✔️ Provides an analytics dashboard

✔️ Runs as a full-stack deployed application

🏷️ Supported Bug Categories

Class	               Description

UI_Error	         Layout issues, misaligned buttons, broken UI elements

Database_Error	   SQL errors, DB connection failures

Network_Error	   Timeouts, connectivity issues

Rendering_Error	   Graphics glitches, blank screens

Crash_Error  	   App crashes, fatal error screens

Other_Error	         Unclassified or rare error types

✨ Key Features

🧠 Automated Bug Triage

Automatically categorizes bug screenshots into real-world error types, eliminating manual inspection and reducing triage time.

🔍 Explainable AI for Trust & Debugging

Generates Grad-CAM heatmaps that visually explain model decisions, helping engineers verify predictions instead of blindly trusting AI.

⚙️ Production-Ready Inference Pipeline

Implements a complete ML inference workflow including preprocessing, prediction, confidence scoring, and result rendering — mirroring real production systems.

📊 Engineering Analytics Dashboard

Tracks prediction history and class distributions, enabling teams to identify recurring bug patterns and systemic issues.

🗃️ Persistent Prediction Logging

Stores inference results with timestamps, supporting auditing, debugging, and future model improvements.

🧩 System Architecture

User Screenshot

      ↓

Image Preprocessing (256×256 RGB)

      ↓

CNN-based Deep Learning Model

      ↓

Softmax Probability Distribution

      ↓

Grad-CAM Explainability

      ↓

Flask Web Application

      ↓

Prediction Logging + Dashboard

🛠️ Tech Stack

ML / DL: TensorFlow, CNN, Grad-CAM

Backend: Python, Flask

Computer Vision: OpenCV, NumPy

Data: SQLite

HOW TO RUN LOCALLY

------------------

1. Create virtual environment (optional but recommended)

   python -m venv venv

2. Activate environment

   (Windows) venv\Scripts\activate

   (Mac/Linux) source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Run Flask server

   python app/app.py

5. Open in browser:

   http://127.0.0.1:5000