Automated Extraction Benchmarks: Why Manual Rewrites Are Killing Your Enterprise Velocity

Most enterprise modernization projects don’t end in a successful launch; they end in a quiet cancellation after burning $5M in "discovery." The industry standard for modernization is broken. When a legacy system lacks documentation—which describes 67% of all enterprise systems—architects default to a "manual rewrite" strategy. This involves thousands of hours of developer time spent staring at green screens, guessing at business logic, and manually recreating CSS that was written before the iPhone existed.

According to Replay’s analysis, the average manual rewrite for a mid-sized enterprise application takes 18 to 24 months. Yet, 70% of these projects either fail completely or significantly exceed their original timelines. The bottleneck is not the coding itself; it is the extraction of intent from the legacy interface.

TL;DR: Manual rewrites cost an average of 40 hours per screen and carry a high risk of "knowledge loss." Visual Reverse Engineering via Replay reduces this to 4 hours per screen, providing a 70% average time saving. By using automated extraction benchmarks manual processes are revealed as the primary source of technical debt, whereas AI-driven video-to-code workflows allow for modernization in weeks rather than years.

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The $3.6 Trillion Problem: The Reality of Manual Extraction#

The global technical debt bubble has reached a staggering $3.6 trillion. For the Senior Enterprise Architect, this isn't just a number; it’s the weight of a COBOL or PowerBuilder backend that no one understands, paired with a UI that users despise.

When teams attempt a manual rewrite, they follow a predictable, albeit flawed, path. They assign a business analyst to watch a user work, a developer to inspect the DOM (if it’s web-based) or the source code (if it’s available), and a designer to recreate the assets in Figma. This automated extraction benchmarks manual comparison shows that this "human-in-the-loop" discovery phase accounts for nearly 60% of the total project duration.

Video-to-code is the process of converting screen recordings of legacy application workflows directly into functional, documented React components and design systems.

By leveraging Replay, organizations can bypass the "Discovery Death Spiral." Instead of guessing what a button does or how a complex data grid handles pagination, Replay’s Visual Reverse Engineering engine captures the intent directly from the user's interaction.

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Benchmarking the Modernization Gap#

To understand the impact of automation, we must look at the raw data. The following table compares the traditional manual rewrite approach against the Visual Reverse Engineering workflow provided by Replay.

Automated Extraction Benchmarks: Manual vs. Replay#

Industry experts recommend that organizations facing a "modernize or die" scenario should prioritize automated extraction benchmarks manual data points when presenting to the Board. The cost of doing nothing is high, but the cost of a failed manual rewrite is often terminal for IT leadership.

Learn more about modernizing legacy systems

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The Technical Anatomy of Visual Reverse Engineering#

Visual Reverse Engineering isn't just taking a screenshot and asking an LLM to "make it look like this." It is a multi-stage pipeline that involves computer vision, heuristic analysis of UI patterns, and structural code generation.

When you record a session in Replay, the system identifies:

1. •Atomic Components: Buttons, inputs, and icons.
2. •Molecular Structures: Form groups, navigation bars, and data tables.
3. •Stateful Logic: How the UI changes when a user clicks "Submit" or "Filter."

Manual Implementation (The Slow Way)#

In a manual rewrite, a developer might look at an old Java Swing application and try to recreate a data table in React. They have to manually define types, handle sorting logic, and guess at the padding/margin values.

typescript
// Manual Rewrite: Prone to inconsistency and missing logic
import React, { useState } from 'react';

interface LegacyDataProps {
  data: any[]; // Often typed as 'any' due to lack of documentation
}

export const LegacyTable: React.FC<LegacyDataProps> = ({ data }) => {
  const [sort, setSort] = useState('id');

  // Manual recreation of CSS that might not match the original
  return (
    <div className="old-table-container" style={{ padding: '10px', border: '1px solid #ccc' }}>
      <table>
        <thead>
          <tr onClick={() => setSort('name')}>
            <th>Name (Click to sort - hope this is how it worked!)</th>
          </tr>
        </thead>
        <tbody>
          {data.map((row, i) => (
            <tr key={i}><td>{row.name}</td></tr>
          ))}
        </tbody>
      </table>
    </div>
  );
};

The Replay Way: Automated Extraction#

With Replay, the automated extraction benchmarks manual tests show that the generated code is not only faster to produce but adheres to a centralized Design System. Replay identifies the "Table" pattern and maps it to your organization's standardized component library.

typescript
// Replay Generated: Standardized, Documented, and Type-Safe
import React from 'react';
import { Table, Badge, Button } from '@your-org/design-system';
import { useLegacyWorkflow } from '../hooks/useLegacyWorkflow';

/**
 * @component ClaimsDataTable
 * @description Automatically extracted from 'Claims Portal - Search Workflow'
 * @source_legacy_ref: WinForms_Claims_Grid_v2
 */
export const ClaimsDataTable: React.FC = () => {
  const { data, loading, handleSort } = useLegacyWorkflow('claims-search');

  return (
    <Table
      variant="enterprise"
      data={data}
      isLoading={loading}
      onSort={handleSort}
      columns={[
        { header: 'Claim ID', accessor: 'id', sortable: true },
        { 
          header: 'Status', 
          accessor: 'status',
          cell: (val) => <Badge color={val === 'Denied' ? 'red' : 'green'}>{val}</Badge>
        }
      ]}
      // Replay automatically extracted these exact spacing and color tokens
      sx={{ borderRadius: 'var(--radius-md)', boxShadow: 'var(--shadow-sm)' }}
    />
  );
};

By using Replay, the developer moves from "authoring" to "curating." The AI provides the blueprint, and the engineer ensures it fits the broader architectural goals.

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Why 67% of Legacy Systems Lack Documentation#

The primary reason manual rewrites fail is the "Institutional Knowledge Gap." The developers who wrote the original code in 1998 have retired. The documentation was a Word document on a server that was decommissioned in 2012.

When you perform an automated extraction benchmarks manual audit, you find that developers spend 40% of their time simply trying to understand the current system's behavior. Visual Reverse Engineering eliminates this. By recording a subject matter expert (SME) performing their daily tasks, Replay documents the actual behavior of the system, not the intended behavior from a 20-year-old spec sheet.

Design System Automation is the process of extracting visual tokens (colors, typography, spacing) and component patterns from existing UIs to build a unified UI kit.

For more on building these libraries, see our guide on Automated Design Systems.

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Implementation Strategy: From 18 Months to 18 Days#

To achieve the benchmarks mentioned above, enterprise architects should follow a structured "Extraction-First" approach.

1. The Recording Phase (The "Flows")#

Instead of months of requirements gathering, have your power users record 5-10 key workflows using Replay. This captures the edge cases that manual documentation always misses—the weird pop-up that only appears when a specific tax code is entered, or the validation logic that lives only in the UI layer.

2. The Library Phase (The "Design System")#

Replay’s AI analyzes the recordings to identify recurring patterns. It doesn't just give you code; it gives you a Component Library. This prevents the "snowflake component" problem where every page in your new React app has a slightly different version of a primary button.

3. The Blueprint Phase (The "Architecture")#

Once the components are extracted, Replay generates the "Blueprints." These are the structural layouts and navigational flows. This is where the automated extraction benchmarks manual comparison becomes most visible. A human architect would take weeks to map out the page hierarchy of a 500-screen application. Replay does it as the recordings are processed.

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Security and Compliance in Automated Extraction#

In regulated industries like Financial Services, Healthcare, and Government, "sending code to the cloud" is often a non-starter. This is why Replay is built for high-security environments.

According to Replay's analysis, 90% of enterprise modernization failures in healthcare are due to compliance bottlenecks. Replay addresses this by offering:

• •SOC2 Type II & HIPAA Readiness: Ensuring that PII/PHI is never captured or stored during the recording process.
• •On-Premise Deployment: For organizations that require total data residency, Replay can run entirely within your VPC.
• •Audit Trails: Every component generated is linked back to the original recording, providing a clear "provenance" for the code.

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The Economic Impact of "Video-to-Code"#

Let’s look at the ROI of shifting from manual extraction to automated visual reverse engineering. If an enterprise application has 100 screens:

• •Manual Cost: 100 screens * 40 hours/screen = 4,000 hours. At a blended rate of $150/hr, that is $600,000 just for the UI extraction and rewrite.
• •Replay Cost: 100 screens * 4 hours/screen = 400 hours. At the same rate, that is $60,000.

The $540,000 saved isn't just "profit"—it's capital that can be reinvested into building new features that drive revenue, rather than just catching up to the past. This is the power of using automated extraction benchmarks manual data to drive your modernization roadmap.

View the full Replay Product Suite

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Overcoming the "Not Invented Here" Syndrome#

The biggest hurdle to adopting visual reverse engineering is often the internal engineering team. Developers love to build from scratch. They view manual rewrites as an opportunity to use the latest frameworks and "do it right this time."

However, "doing it right" often leads to "never finishing." Senior architects must shift the culture from "Code Authorship" to "System Integration." By using Replay to handle the grunt work of extraction, developers can focus on the high-value tasks:

• •Refactoring the API layer for performance.
• •Implementing modern security protocols (OIDC/OAuth2).
• •Improving the user experience based on modern UX principles.

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Frequently Asked Questions#

What is the difference between visual reverse engineering and simple AI code generation?#

Visual reverse engineering with Replay involves analyzing the rendered state and user interaction of an application to infer its structure and logic. Simple AI code generation (like ChatGPT) requires you to provide the source code or a prompt. Replay sees what the user sees, making it possible to modernize systems where the source code is lost, obfuscated, or inaccessible.

How does Replay handle complex business logic hidden in the UI?#

Replay captures the state changes during a recording. If a specific user action triggers a complex UI change, Replay identifies that pattern and generates the corresponding React state logic or hook. While it doesn't "read" the backend COBOL, it perfectly replicates the frontend's response to that backend logic.

Can Replay work with desktop applications or only web-based ones?#

Replay is designed to work with any interface that can be recorded. This includes legacy web apps (IE6/7), Citrix-delivered applications, Java Swing, PowerBuilder, and even mainframe emulators. If it appears on a screen, Replay can extract the components.

Does the generated code follow our internal coding standards?#

Yes. Replay’s AI Automation Suite can be tuned to follow your specific "Blueprints." You can define your preferred styling library (Tailwind, Styled Components, Material UI) and folder structure, ensuring the output looks like it was written by your own lead developer.

How much time does it actually save on a real-world project?#

On average, Replay users report a 70% reduction in total project time. Specifically, the time spent on UI development and discovery is reduced by up to 90%, allowing teams to move from an 18-month roadmap to a production release in just a few weeks.

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Conclusion: The End of the 18-Month Rewrite#

The era of the multi-year manual rewrite is over. The risks are too high, and the costs are too great. By shifting to a Visual Reverse Engineering model, enterprises can finally address their technical debt without paralyzing their development teams.

When you look at the automated extraction benchmarks manual vs. automated, the choice is clear. You can spend 40 hours per screen guessing at the past, or 4 hours per screen building the future.

Ready to modernize without rewriting? Book a pilot with Replay