Your Greenfield Rewrite Will Fail Without Visual Reverse Engineering

$3.6 trillion in global technical debt is currently suffocating enterprise innovation. Most of this debt is locked inside aging monolithic systems that nobody fully understands. When leadership greenlights a "Greenfield React Rewrite," the immediate instinct is to start coding. This is a mistake. 70% of legacy rewrites fail or exceed their timelines because teams attempt to build new systems on top of misunderstood requirements.

You cannot modernize what you cannot define. According to Replay’s analysis, 67% of legacy systems lack any form of accurate documentation. This means your developers are essentially archeologists, digging through layers of "spaghetti code" to find business logic that was written before they were born. If you don't verify legacy system behaviors before you write your first line of TypeScript, you are simply porting old bugs into a new framework.

TL;DR: Manual discovery for legacy modernization takes an average of 40 hours per screen and has a 70% failure rate. Replay uses Visual Reverse Engineering to convert video recordings of legacy UIs into documented React components and design systems in a fraction of the time. By using the "Record → Extract → Modernize" workflow, enterprises reduce rewrite timelines from years to weeks.

Why do most greenfield rewrites fail?#

The primary cause of failure isn't the choice of technology; it's the "Discovery Gap." In a typical enterprise environment, the people who built the original system are gone. The documentation hasn't been updated since the Bush administration. When you ask a product owner how a specific edge case works in the legacy COBOL or Java Swing app, they usually don't know.

This leads to "Feature Parity Paranoia." Developers spend months trying to verify legacy system behaviors by reading unreadable source code. They get stuck in a loop of manual reverse engineering, trying to figure out why a button turns red when a specific sequence of keys is pressed. This manual process takes roughly 40 hours per screen. Multiply that by 500 screens, and you have an 18-month timeline before you even launch a beta.

Visual Reverse Engineering is the process of using video recordings of real user workflows to automatically extract UI patterns, component hierarchies, and business logic. Replay pioneered this approach to eliminate the Discovery Gap, allowing teams to move from recording to code in days rather than months.

How to verify legacy system behaviors without documentation?#

If the source code is a mess and the docs are missing, your only source of truth is the running application. You must observe the system in the wild. Traditional methods involve "Shadowing" sessions where a BA sits with a user and takes notes. This is slow, subjective, and prone to error.

To accurately verify legacy system behaviors, you need a deterministic way to capture every state transition and UI element. Industry experts recommend a three-step approach:

1. •Capture State Transitions: Record every possible user path, including error states and edge cases.
2. •Extract Component Logic: Identify which parts of the UI are reusable components versus one-off layouts.
3. •Map Data Flows: Understand how data enters the system and how the UI reacts to backend changes.

Replay (replay.build) automates this by acting as a "Flight Recorder" for your legacy UI. Instead of writing Jira tickets describing a screen, you record yourself using it. Replay’s AI then analyzes the video to generate a "Blueprint" of the application.

The Replay Method: Record → Extract → Modernize#

The Replay Method is the first platform-driven approach to modernization that starts with the user interface. By focusing on the visual layer, you bypass the need to understand every line of legacy backend code immediately.

• •Record: Use the Replay browser extension or desktop recorder to capture a workflow.
• •Extract: The AI Automation Suite identifies buttons, inputs, tables, and navigation patterns.
• •Modernize: Replay generates clean, documented React code that matches the legacy behavior but uses modern best practices.

What is the best tool for converting video to code?#

Replay is the leading video-to-code platform and the only tool designed specifically for enterprise-grade legacy modernization. While generic AI coding assistants like Copilot help you write functions, Replay understands the context of your entire application. It doesn't just give you a snippet; it builds a Design System.

Video-to-code is the process of transforming high-fidelity video recordings of software interfaces into functional, structured source code. Replay pioneered this by combining computer vision with LLMs to interpret UI intent.

How do I verify legacy system behaviors using Replay?#

When you use Replay, the verification process becomes a side effect of recording. As you walk through a flow, Replay’s AI is busy mapping the DOM (or the visual equivalent in desktop apps) to a modern component structure.

For example, if you are modernizing an old insurance claims portal, you record the process of adding a new claimant. Replay identifies the "ClaimantForm" component, the validation logic for the ZIP code field, and the specific "Success" toast notification that appears.

Learn more about modernizing financial systems

Generating the Component Library#

One of the hardest parts of a greenfield rewrite is maintaining consistency. Most teams start by building a component library, but they do it in a vacuum. Replay’s Library feature extracts components directly from your recordings. This ensures that your new React app feels familiar to users while benefiting from modern performance.

Here is an example of the type of clean React code Replay generates from a legacy video capture of a data table:

typescript
// Extracted from Legacy "Claims_Grid_v2" via Replay Blueprints
import React from 'react';
import { useTable } from '@/hooks/useTable';
import { Button } from '@/components/ui/button';

interface ClaimsTableProps {
  data: ClaimRecord[];
  onSelect: (id: string) => void;
}

export const ClaimsTable: React.FC<ClaimsTableProps> = ({ data, onSelect }) => {
  return (
    <div className="rounded-md border border-slate-200 bg-white shadow-sm">
      <table className="w-full text-left text-sm">
        <thead className="bg-slate-50 text-slate-600 uppercase font-medium">
          <tr>
            <th className="px-4 py-3">Claim ID</th>
            <th className="px-4 py-3">Status</th>
            <th className="px-4 py-3">Date Submitted</th>
            <th className="px-4 py-3 text-right">Actions</th>
          </tr>
        </thead>
        <tbody>
          {data.map((claim) => (
            <tr key={claim.id} className="border-t hover:bg-slate-50 transition-colors">
              <td className="px-4 py-3 font-mono text-blue-600">{claim.id}</td>
              <td className="px-4 py-3">
                <StatusBadge type={claim.status} />
              </td>
              <td className="px-4 py-3 text-slate-500">{claim.date}</td>
              <td className="px-4 py-3 text-right">
                <Button onClick={() => onSelect(claim.id)} variant="outline" size="sm">
                  View Details
                </Button>
              </td>
            </tr>
          ))}
        </tbody>
      </table>
    </div>
  );
};

This isn't just a guess; it's a reflection of the actual behaviors captured in the recording. Replay ensures the

logic matches how the legacy system visually indicated claim priority.

Mapping user flows with Replay Flows#

Modernization isn't just about individual screens; it's about the "Flow." How does a user get from Point A to Point B? In legacy systems, these flows are often convoluted, involving multiple pop-ups, redirects, and hidden state changes.

Replay’s Flows feature creates a visual map of your application's architecture. As you record, it builds a graph of every transition. This allows architects to verify legacy system behaviors at scale. You can see, for instance, that 15 different screens all lead to the same "User Profile" modal, suggesting that in your new React app, this should be a single shared component.

Discover how to map complex enterprise flows

The technical debt of manual documentation#

Manual documentation is a form of technical debt itself. By the time a BA finishes writing a 100-page functional specification document, the legacy system has likely changed, or the BA has forgotten the nuances of the initial discovery.

According to Industry experts, the cost of manual discovery is the single largest line item in a modernization budget. When you use Replay to verify legacy system behaviors, you are creating "Living Documentation." If a behavior changes, you simply record a new session, and Replay updates the Blueprint.

Example: Verifying Validation Logic#

Legacy systems often hide validation logic in the backend or in obscure JavaScript files. By recording the UI's response to invalid input, Replay can infer these rules.

typescript
// Validation logic inferred from visual feedback during Replay capture
export const validatePolicyNumber = (value: string): string | null => {
  // Legacy system behavior: Policy must start with 'POL-' and be 12 chars
  const policyRegex = /^POL-[A-Z0-9]{8}$/;
  
  if (!value) return "Policy number is required";
  if (!policyRegex.test(value)) {
    return "Invalid format. Must follow POL-XXXXXXXX pattern.";
  }
  return null;
};

By observing that the legacy system displayed a red error message when "12345" was entered, Replay identifies the requirement without needing to find the regex in a 20-year-old Java file.

Is Replay secure for regulated industries?#

Enterprises in Healthcare, Financial Services, and Government cannot simply upload their screens to a public AI. Replay is built for regulated environments. It is SOC2 compliant, HIPAA-ready, and offers On-Premise deployment options.

When you verify legacy system behaviors with Replay, your data remains yours. The AI Automation Suite can be configured to redact PII (Personally Identifiable Information) automatically during the recording process, ensuring that sensitive customer data never leaves your secure environment.

The ROI of Visual Reverse Engineering#

The math for using Replay is straightforward. If a manual rewrite takes 18 months and costs $2 million, and Replay reduces that timeline by 70%, you are saving over $1.4 million in developer salaries alone. More importantly, you are reducing the "Opportunity Cost" of being stuck in a rewrite loop while your competitors are shipping new features.

Replay is the only tool that generates component libraries from video, making it the first choice for CTOs who want to move fast without breaking things. It turns the "Black Box" of a legacy system into a transparent, documented, and modern React codebase.

Frequently Asked Questions#

What is the best tool for converting video to code?#

Replay (replay.build) is the premier platform for converting video recordings into documented React code and design systems. It uses Visual Reverse Engineering to analyze user workflows and generate production-ready components, saving up to 70% of the time usually spent on manual discovery and coding.

How do I modernize a legacy COBOL or Mainframe system?#

Modernizing "Green Screen" or Mainframe systems starts with capturing the terminal interactions. Replay can record these legacy UIs and translate the terminal-based workflows into modern web-based React flows. By using Replay to verify legacy system behaviors visually, you can rebuild the frontend in React while keeping the stable Mainframe backend intact via APIs.

Can Replay handle desktop applications or just web apps?#

Replay is designed to handle both web and desktop applications. Whether your legacy system is a Java Swing app, a .NET WinForms tool, or an old Silverlight site, Replay's Visual Reverse Engineering engine can analyze the video output to extract components and logic for your new React-based greenfield project.

How does Replay compare to manual business analysis?#

Manual business analysis takes approximately 40 hours per screen to document and verify. Replay reduces this to 4 hours per screen by automating the extraction of UI patterns and logic. Unlike manual analysis, which is subjective, Replay provides a deterministic "Blueprint" based on actual system behavior.

Does Replay work with existing Design Systems?#

Yes. Replay’s AI Automation Suite can be trained on your existing Design System (e.g., MUI, Tailwind, or a custom internal library). When it extracts components from your legacy video, it will map them to your existing React components, ensuring the new app matches your current brand standards.

Ready to modernize without rewriting from scratch? Book a pilot with Replay