Software Behaviour Predictor
Software issue tracking systems collect large volumes of text-based bug reports and user complaints
Preview Gallery
2 media
Technologies & Skills
Limited time offer
What's Included
Support & Customization
Resource Links
Purchase this project to unlock source and premium resources. Document/report remain secure preview-based on this page.
🎯 Project Title
Predictive Modeling of Software Issue Types using LSTM
📚 Technologies Used
Frontend: HTML, CSS, Bootstrap
Backend: Python, Django (Web Framework)
Database: MySQL (via WAMP Server)
Machine Learning: LSTM (Keras, TensorFlow)
Deployment: Localhost using WAMP Server
Tools & Libraries:
pandas, numpy
scikit-learn
TensorFlow / Keras
Django REST Framework
🔍 Problem Statement
Software issue tracking systems collect large volumes of text-based bug reports and user complaints. Manually labeling and prioritizing them is time-consuming and error-prone.
This project automates the classification of issue types such as:
Enhancement
Bug
Documentation
Feature Request
Query
✅ Key Features
Multi-label text classification using LSTM
Web-based interface to input and predict issue type
Accuracy and probability display for each class
User-friendly interface
Integrated with MySQL using Django models
Trained on real-world dataset
🏗️ System Architecture
User Input (Text Report)
Preprocessing
LSTM Model Prediction
Display Predicted Labels
Store & Display History (Optional)
Future Enhancements
Known Issues
Installation
How to Run (Local Setup)
1. Clone the Repository
git clone https://github.com/your-username/software-predictor.git cd software-predictor
Setup Python Environment pip install -r requirements.txt
Setup Database Start WAMP Server
Create a new MySQL database: predictive_modeling_of_software_behavior
Run Django migrations:
python manage.py makemigrations python manage.py migrate
Start Server python manage.py runserver Visit: http://127.0.0.1:8000/ to use the application.
Usage
Clone the Repository
git clone https://github.com/your-username/software-predictor.git cd software-predictor
Setup Python Environment pip install -r requirements.txt
Setup Database Start WAMP Server
Create a new MySQL database: predictive_modeling_of_software_behavior
Run Django migrations:
python manage.py makemigrations python manage.py migrate
Start Server python manage.py runserver Visit: http://127.0.0.1:8000/ to use the application.
System Requirements
Minimum Hardware Requirements
- Processor: Intel Core i5 (7th Gen) or AMD Ryzen 5
- RAM: 8 GB DDR4 (Necessary for running Django, MySQL, and ML models concurrently)
- Storage: 20 GB available HDD/SSD space
- GPU: Not required (Standard integrated graphics are sufficient for basic ML inference)
Recommended Hardware Requirements
- Processor: Intel Core i7 (10th Gen) or AMD Ryzen 7 (For faster ML model training)
- RAM: 16 GB DDR4 (Smooth multitasking between IDE, Server, and Data Processing)
- Storage: 50 GB available SSD space (Speeds up MySQL queries and dataset loading)
Software Requirements
- Operating System: Windows 10/11, macOS, or Linux (Ubuntu 20.04+)
- Environment: Python 3.8 to 3.11 (Matching typical Django + ML library compatibility)
- Database Server: WAMP Server 3.x (with MySQL 5.7+ or MariaDB)
- Web Framework: Django 4.x or 5.x
- Version Control: Git (For cloning the repository)
- Supported Browsers: Google Chrome, Mozilla Firefox, or Microsoft Edge
Python Dependencies (Included in requirements.txt)
- Core Framework:
django - Database Connector:
mysqlclientorpymysql - Data Processing:
numpy,pandas - Machine Learning:
scikit-learn(and optionallyscipy,joblibfor saving models) - Visualization:
matplotlib,seaborn
Slides Open in New Tab
For better readability, slides are opened directly. Documents remain preview-only with secure backend rendering.
Showing preview pages only. Purchase for full access to all pages and complete source package.
Login for Full AccessNo Q&A available yet
Be the first to ask a question!
Ask a Question
Customer Reviews
Write Your Review
No reviews yet
Be the first to review this project!
Similar Projects
You might also be interested in these projects
AI & Machine Learning
Vehicle Tracking using YOLOv8 and OpenCV
Real-time vehicle tracking using YOLOv8 and a Constant Velocity Motion Model for accurate multi-object tracking in traffic videos.
AI & Machine Learning
AI PDF Assistant – RAG Chatbot using LlamaIndex & Qdrant
An AI-powered RAG chatbot that answers questions from uploaded PDF documents using vector search and LLMs.
AI & Machine Learning
sentiment analysis with sarcasm
sarcasm detection ia a advanced NLP task that typically requires building a custom machine learning and deep learning model
AI & Machine Learning
VERIDYN — AI-Powered Productivity & Behavioral Analytics Platform
AI-powered productivity platform with behavioral analytics, LLM-driven insights, and real-time statistical tracking across 8 REST endpoints