Code Whisperer Time Machine

[Lomanu4]

Project: "Code Whisperer Time Machine"
Transform legacy codebases into modern, AI-interactive, self-documenting systems

Problem Statement
Developers often struggle with understanding how and why code evolved over time, especially in large or legacy codebases. Tracking changes, understanding intent, and maintaining documentation is inefficient and time-consuming.

? Concept:
Build a tool that takes a real legacy codebase (e.g., COBOL, Perl, or early Java) and uses Amazon Q Developer to:

Translate it into modern languages (e.g., TypeScript, Python or Go)

Use Amazon Q as an AI co-pilot to explain functions, suggest rewrites, and turn old spaghetti code into clean microservices or serverless functions.

Solution Overview
Code Whisperer Time Machine is an intelligent tool that brings version control history to life. It enables developers to explore, visualize, and understand the evolution of their code with AI-powered insights.

Key Features & Functionality
? Time Machine UI: Scrollable, interactive commit timeline

? AI Commit Whispering: Summarizes the intent behind changes

?️ Visual Diff Engine: Highlights changes at file, function and logic levels

? Smart Search: Search history by feature, keyword or behavior

? Auto-Documentation: Generates markdown changelogs or inline comments

? Demo Flow:
User uploads old codebase (e.g., COBOL or PHP app)

System analyzes it with Q Developer

UI shows:

Suggested modernized architecture (e.g., serverless + event-driven)

User can approve code transformations step-by-step

Outputs:

Transformed TypeScript/Python code

CDK infrastructure-as-code

? Why it's a great fit:
Feels "impossible" — AI-powered reverse engineering + full-stack transformation

Explores deep capabilities of Amazon Q (understanding + generation)

Useful for real-world modernization efforts (e.g., governments, banks)

Visually impressive and interactive — perfect for demos

Quick Starter Example (Node.js → Python):
You can prompt Q Developer with:


# Use Q Developer to translate this Node.js function:
function calculateTax(income) {
if (income < 10000) return 0;
return income * 0.2;
}

calculate_tax = lambda income: 0 if income < 10000 else income * 0.2

// File: package.json
setTimeout(() => mermaid.init(undefined, ".mermaid"), 0);
};

return (
<div className="p-6 font-mono">
<h1 className="text-2xl mb-4">?️ Code Whisperer Time Machine</h1>
<textarea
rows={8}
className="w-full p-2 border"
value={legacyCode}
onChange={(e) => setLegacyCode(e.target.value)}
/>
<button className="mt-2 p-2 bg-blue-600 text-white rounded" onClick={handleAnalyze}>
Analyze & Translate
</button>

{explanation && (
<div className="mt-4">
<h2 className="font-bold">Explanation:</h2>
<p>{explanation}</p>
</div>
)}

{translated && (
<div className="mt-4">
<h2 className="font-bold">Translated Python Code:</h2>
<pre className="bg-gray-100 p-2">{translated}</pre>
</div>
)}

<div className="mt-4">
<h2 className="font-bold">Architecture Diagram:</h2>
<div className="mermaid">{diagram}</div>
</div>
</div>
);
};

export default App;

I've scaffolded a working project called "Code Whisperer Time Machine" that simulates legacy code translation and visualization using React, TypeScript and Mermaid.js.

To run the demo:


npm install
npm run dev

Then open your browser at

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to use the app.

** Tech Stack:**
Amazon Q Developer (code translation, refactoring, understanding)

AWS CDK + Lambda / Step Functions (to modernize functionality)

React + TypeScript front-end (to visualize the AI-guided transformation)

Frontend: React, Tailwind CSS, D3.js (for timeline visualization)

Backend: Node.js / Python (Flask or FastAPI)

AI Layer: OpenAI GPT (for commit summaries)

Deployment: Docker, Vercel / Heroku

Target Audience
Software engineers maintaining legacy code
DevOps teams tracking critical code changes
Engineering managers reviewing pull requests
Open-source contributors

Demo Walkthrough
Step 1: Connect your Git repository
Step 2: Browse the timeline
Step 3: Select a commit to view AI explanations
Step 4: Compare versions visually
Step 5: Export AI documentation

Benefits
Saves hours of code review time
Improves onboarding for new developers
Bridges communication between teams
Reduces technical debt by contextualizing history

Future Enhancements
Multi-repo tracking
Integration with GitHub, GitLab, Bitbucket
Real-time collaborative annotation

Conclusion
Code Whisperer Time Machine turns your Git history into a living, learning assistant — making your past code clearer, smarter, and more accessible.

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