AhmadHassan-BTed/CoDiver

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.

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.

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.

graph TD
    classDef frontend fill:#1e3a8a,stroke:#3b82f6,stroke-width:2px,color:#fff;
    classDef controller fill:#475569,stroke:#94a3b8,stroke-width:2px,color:#fff;
    classDef engine fill:#0f172a,stroke:#10b981,stroke-width:2px,color:#fff;
    
    UI[ Streamlit Frontend]:::frontend -->|Unified File Upload| Controller[ Universal Control Center]:::controller
    
    Controller -->|Routes .py| PythonEngine[ Python AST Engine]:::engine
    Controller -->|Routes .java| JavaEngine[ Java AST Engine]:::engine
    Controller -->|Routes .json| AgileEngine[ Agile Process Engine]:::engine
    
    subgraph Core Analyzers
        PythonEngine --> PyMetrics[Complexity & Code Metrics]
        JavaEngine --> JavaSmells[Design & Implementation Smells]
        AgileEngine --> SprintMetrics[Velocity & Scope Creep]
    end
    
    PyMetrics --> Aggregator[ Results Aggregator]
    JavaSmells --> Aggregator
    SprintMetrics --> Aggregator
    
    Aggregator -->|Pandas DataFrames| UI

Data flows deterministically from the browser, through the AST generators, into Pandas dataframes, and back out to Plotly visuals. The UI remains fully stateless until explicitly commanded to execute an analysis.

sequenceDiagram
    participant User as Developer
    participant UI as  UI (app.py)
    participant Engine as  Analyzer Engine
    participant State as  Session State
    
    User->>UI: Uploads Source Code (.py, .java)
    UI->>UI: Auto-categorize by extension
    User->>UI: Adjusts Global Parameters
    User->>UI: Executes CoDiver Analysis
    
    UI->>Engine: Dispatches raw UTF-8 strings & configs
    
    rect rgb(30, 41, 59)
        Note right of Engine: Backend Processing Phase
        Engine->>Engine: Generate Abstract Syntax Trees
        Engine->>Engine: Traverse classes, fields, methods
        Engine->>Engine: Evaluate Code Smells & Constraints
    end
    
    Engine-->>State: Yield structured dictionaries
    State->>State: Transform to Pandas DataFrames
    State-->>UI: Triggers reactive re-render
    
    UI-->>User: Displays interactive visualizations

The repository enforces a strict separation between UI logic and calculation algorithms to guarantee high cohesion and maintainability at scale.

CoDiver/
├── app.py                      # Master Unified Controller & UI Entry Point
├── requirements.txt            # Dependency configuration
└── src/
    ├── java_analyzer/          # Java Static Analysis Subsystem
    │   ├── analyzer_engine.py  # javalang AST traversal logic
    │   ├── dashboard.py        # Session state & execution handlers
    │   ├── detectors/          # Code Smell logic (Design, Naming, etc.)
    │   └── utils/              # UI components & helpers
    │
    └── software_metrics/       # Python & Agile Analytics Subsystem
        ├── calculators/        # Core calculation algorithms (COCOMO, Agile)
        ├── ui/                 # Visualization components (Charts, Cards)
        └── tests/              # Subsystem validation

Because this application leverages a robust Python backend for intensive AST processing, it is deployed natively via containerized Python runtimes (and cannot be served statically via standard GitHub Pages).

The platform is currently live and operational on Streamlit Community Cloud.

Access the live suite here: codiver.streamlit.app

Because this application leverages a robust Python backend for intensive AST processing, it is deployed natively via containerized Python runtimes.

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.

To initialize the suite in a local development environment:

# 1. Clone the repository
git clone https://github.com/yourusername/CoDiver.git
cd CoDiver

# 2. Install AST parsers and UI dependencies
pip install -r requirements.txt

# 3. Boot the reactive frontend server
streamlit run app.py

New ideas, architectural optimizations, and structural improvements are warmly welcomed. The system is designed to be highly extensible - adding support for a new language parser simply requires introducing a new module within src/ and wiring it to the Universal Control Center in app.py.

When modifying the codebase, please ensure that UI concerns remain strictly separated from backend AST calculation logic.

Ahmad Hassan (B-Ted)
Software Engineer

This architecture was designed and engineered from the ground up to solve complex software quality tracking constraints using modern, event-driven Python web paradigms.

This repository is published under the MIT License, promoting open knowledge and collaborative engineering.