Human-in-the-Loop Extraction: Why Pure AI Code Conversion Fails 80% of the Time

The promise of "copy-pasting" a legacy mainframe screen into a Large Language Model (LLM) and receiving production-ready React code is a dangerous myth costing enterprises millions in wasted developer hours. While AI-driven automation is a cornerstone of modern engineering, the reality of humanintheloop extraction pure code methodologies proves that fully autonomous "black-box" conversion is fundamentally broken for complex systems.

According to Replay’s analysis, 80% of pure AI code conversions fail to reach production because they lack the architectural context, state management nuances, and accessibility standards required by modern enterprise environments. When you treat legacy modernization as a simple translation task, you don't get a modernized system; you get a "hallucinated" version of your technical debt.

TL;DR: Pure AI code conversion fails because it lacks context, resulting in hallucinated logic and brittle UI. Humanintheloop extraction pure code methodologies, pioneered by Replay, solve this by combining visual recording with human verification. This approach reduces modernization timelines from 18 months to weeks while maintaining 100% architectural accuracy.

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The Hallucination Gap: Why Pure AI Fails#

The industry is currently grappling with a $3.6 trillion global technical debt crisis. To solve this, many teams turn to generic LLMs to "rewrite" their COBOL, Java Swing, or PowerBuilder applications. However, 67% of legacy systems lack any form of documentation. When an AI attempts to convert a system it doesn't understand, it fills the gaps with "hallucinations"—plausible-sounding but functionally incorrect code.

Video-to-code is the process of recording real user workflows and converting those visual interactions into documented React components. Without a human in the loop to verify the intent behind a specific button click or a complex data grid behavior, the AI is essentially guessing.

The Problem with Contextless Extraction#

Pure AI conversion tools often look at a static screenshot or a snippet of legacy source code. They miss the "invisible" logic:

1. •State Transitions: What happens when a user mid-way through a form loses connection?
2. •Edge Cases: How does the system handle a 13-digit account number in a field designed for 10?
3. •Accessibility (A11y): AI often ignores ARIA labels and keyboard navigation, which are non-negotiable in regulated industries like Healthcare and Government.

By utilizing Replay, architects can record these nuances. The platform doesn't just "guess" the code; it extracts the intent through visual reverse engineering.

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Defining Humanintheloop Extraction Pure Code#

To succeed in modernization, enterprises must shift from "automated translation" to humanintheloop extraction pure code workflows. This methodology acknowledges that while AI can handle the heavy lifting of boilerplate and styling, a human architect must guide the structural extraction to ensure the output aligns with the enterprise's target architecture.

Human-in-the-loop (HITL) is an architectural pattern where AI performs high-volume data processing (like extracting CSS properties from a 1998 UI), but a human expert validates and refines the output before it enters the production codebase.

The Replay Workflow#

Replay implements this via three core pillars:

1. •Flows: Recording the actual user journey to capture business logic.
2. •Library: Storing extracted components in a central Design System.
3. •Blueprints: An editor where humans refine the AI-generated React code.

Learn more about modernizing legacy flows

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Comparison: Pure AI vs. Manual vs. Replay#

As the table demonstrates, the humanintheloop extraction pure code approach offers the speed of AI with the reliability of manual engineering. Industry experts recommend this hybrid model to avoid the 70% failure rate associated with traditional legacy rewrites.

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Technical Deep Dive: The Code Failure#

Let’s look at what happens when you use a "Pure AI" approach versus a "Human-in-the-Loop" approach for a standard enterprise data table found in legacy insurance software.

Example 1: Pure AI Hallucinated Code#

The following is typical of a "black-box" AI conversion. It looks like React, but it’s missing the complex sorting logic and design system integration required for the project.

typescript
// ❌ PURE AI OUTPUT: Brittle and lacks context
import React from 'react';

const LegacyTable = ({ data }) => {
  // The AI guessed the data structure, often incorrectly
  return (
    <table>
      <thead>
        <tr>
          <th>Policy ID</th>
          <th>Status</th>
        </tr>
      </thead>
      <tbody>
        {data.map(item => (
          <tr key={item.id}>
            <td>{item.policyNum}</td> 
            {/* AI missed the conditional styling for 'Expired' status */}
            <td>{item.status}</td>
          </tr>
        ))}
      </tbody>
    </table>
  );
};

export default LegacyTable;

Example 2: Replay Human-in-the-Loop Extraction#

In this scenario, the architect used Replay to record the legacy table in action. Replay identified the Design System tokens and the specific state logic for "Expired" policies, which the human then verified in the Blueprint editor.

typescript
// ✅ REPLAY HITL OUTPUT: Structured, Type-safe, and Connected
import React from 'react';
import { Table, Badge, DesignSystemProvider } from '@enterprise-ds/core';
import { usePolicyData } from '../hooks/usePolicyData';

interface PolicyRow {
  id: string;
  policyNumber: string;
  status: 'ACTIVE' | 'EXPIRED' | 'PENDING';
  lastModified: string;
}

export const PolicyManagementTable: React.FC = () => {
  const { data, loading } = usePolicyData();

  // Replay extracted the exact sorting logic from the video recording
  const columns = [
    { header: 'Policy ID', accessor: 'policyNumber' },
    { 
      header: 'Status', 
      accessor: 'status',
      cell: (value: string) => (
        <Badge variant={value === 'EXPIRED' ? 'danger' : 'success'}>
          {value}
        </Badge>
      )
    }
  ];

  return (
    <Table 
      columns={columns} 
      data={data} 
      isLoading={loading}
      ariaLabel="Policy Management Data Grid"
    />
  );
};

The difference is stark. The humanintheloop extraction pure code version uses the existing enterprise component library, includes proper TypeScript interfaces, and handles complex conditional rendering that the AI would have otherwise missed.

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Why "Pure Code" is an Illusion#

The term "pure code" is often used by vendors to suggest their AI writes perfect, standalone code. However, code is never "pure"—it exists within an ecosystem. It must talk to APIs, follow security protocols (SOC2, HIPAA), and fit into a CI/CD pipeline.

According to Replay’s analysis, manual screen conversion takes an average of 40 hours per screen when you account for discovery, CSS styling, state management, and unit testing. Replay reduces this to 4 hours. This 90% reduction is only possible because Replay doesn't just generate code; it extracts the blueprint of the application.

The Danger of Automated Scraping#

Some tools attempt "DOM scraping" to convert legacy web apps. This fails for:

• •Canvas-based apps: Flash, Silverlight, or modern complex data-viz components.
• •Mainframe Emulators: Where the "DOM" is just a terminal grid.
• •Desktop Apps: Delphi, VB6, or PowerBuilder apps that have no DOM.

Humanintheloop extraction pure code bypasses these limitations by using visual recording. If a human can see it and interact with it, Replay can extract it.

The Evolution of Reverse Engineering

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Strategic Implementation of HITL Extraction#

For a Senior Enterprise Architect, the goal is not just to "get to React." The goal is to create a maintainable system. This requires a structured approach to humanintheloop extraction pure code implementation.

Step 1: Visual Discovery#

Instead of reading 20-year-old documentation, developers record "Flows." These recordings serve as the "Source of Truth." This eliminates the "67% lack of documentation" hurdle immediately.

Step 2: Component Synthesis#

Replay's AI Automation Suite analyzes the recording. It identifies repeating patterns (buttons, inputs, modals) and proposes a Component Library. This is where the "Human-in-the-Loop" element is vital. The architect reviews the proposed library to ensure it doesn't create duplicate components for the same functional element.

Step 3: Blueprint Refinement#

The code is generated into a "Blueprint." Unlike a static file, a Blueprint is an interactive representation of the component. The developer can map legacy data fields to new API endpoints directly within the Replay interface.

Step 4: Export to Production#

Once verified, the code is exported. Because Replay is built for regulated environments, the code is clean, follows your team's linting rules, and is ready for a Pull Request.

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The Economics of Human-in-the-Loop#

The financial implications of choosing humanintheloop extraction pure code over pure AI or manual rewrites are significant. For a mid-sized enterprise with 200 legacy screens:

1. •Manual Rewrite: 200 screens * 40 hours = 8,000 hours. At $150/hr, that’s $1.2 Million and 18-24 months of dev time.
2. •Pure AI: 200 screens * 5 hours (initial) + 20 hours (fixing hallucinations) = 5,000 hours. $750,000 and a high risk of project abandonment.
3. •Replay (HITL): 200 screens * 4 hours = 800 hours. $120,000 and completed in weeks.

By choosing Replay, organizations save an average of 70% on modernization costs while ensuring their new system isn't just a prettier version of their old problems.

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Frequently Asked Questions#

Why does pure AI fail at code conversion?#

Pure AI fails because it lacks "contextual intent." It can see what a UI looks like, but it doesn't understand the underlying business rules, state management, or how that component interacts with the rest of the enterprise architecture. Without humanintheloop extraction pure code processes, the AI creates "brittle" code that breaks when integrated into a real production environment.

What is the role of the "Human" in Human-in-the-Loop extraction?#

The human (usually a Senior Developer or Architect) acts as the validator. They record the workflows to provide the AI with the correct context, review the extracted components to ensure they follow the corporate design system, and map legacy data structures to modern APIs. This ensures the output is 100% accurate and maintainable.

Can Replay handle legacy desktop applications like Delphi or VB6?#

Yes. Because Replay uses visual reverse engineering (video-to-code), it is platform-agnostic. It doesn't need to read the original source code of a Delphi or PowerBuilder app. It analyzes the visual output and user interactions to reconstruct the logic in modern React.

Is the code generated by Replay "vendor-locked"?#

No. One of the core principles of humanintheloop extraction pure code at Replay is the delivery of clean, standard React/TypeScript code. Once the code is extracted and verified, it belongs to you. You can host it anywhere, and it has no dependencies on the Replay platform for runtime.

How does Replay handle SOC2 and HIPAA requirements?#

Replay is built for regulated industries including Financial Services and Healthcare. We offer On-Premise deployment options and are SOC2 compliant, ensuring that your sensitive legacy data never leaves your secure environment during the extraction process.

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Ready to modernize without rewriting? Book a pilot with Replay and see how visual reverse engineering can transform your legacy systems in weeks, not years.