Co Diver

Abstract

1. Introduction

Software is inherently human. Behind every line of code, every architectural decision, and every sprint metric lies human effort and engineering intent. The CoDiver: Code Sniffer & Project Analyzer was built on the premise that technical debt shouldn't be an abstract concept - it should be deeply visible, understandable, and actionable.

The platform operates on a Zero-Coupling, Event-Reactive Architecture. The frontend strictly delegates analysis to isolated backend engines, which parse raw files into Abstract Syntax Trees (ASTs) on the fly.

This platform bridges the gap between raw codebase complexity and human understanding. By unifying Python parsing, Java static analysis, and Agile process tracking into a single frictionless dashboard, the suite abstracts away the borders between languages and focuses purely on the code.

2. Objective

An advanced, unified platform for code quality, architectural metrics, and Agile analytics. Features Python AST parsing, Java static analysis (code smells), and interactive project health dashboards built with Streamlit.

This project demonstrates how Python, Streamlit can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/AhmadHassan-BTed/CoDiver.git
cd CoDiver

1. Fork this repository.
2. Navigate to Streamlit Share.
3. Connect your fork and point the main file path to app.py.
4. The cloud environment will automatically parse requirements.txt and deploy your instance.

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by AhmadHassan-BTed and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.