The Hidden Dangers of Using Generative AI Without Runtime UI Context for React Conversion

Your Large Language Model (LLM) has no idea what happens when a user clicks "Submit" on a 1998 Java Applet with a hidden 300ms race condition. It cannot see the micro-interactions, the ephemeral state transitions, or the complex validation logic buried in a legacy mainframe's terminal emulator. When enterprise architects attempt to modernize legacy systems by feeding static screenshots or snippets of archaic code into a standard generative AI, they aren't just taking a shortcut—they are building a house on a foundation of "hallucinated" logic.

The industry is currently grappling with a $3.6 trillion global technical debt, and the rush to solve it via "blind" AI is creating a new category of risk. While the promise of instant code generation is alluring, the hidden dangers using generative AI in a vacuum—without runtime UI context—often result in components that look like the original but behave like a broken prototype.

TL;DR:

• •Generative AI without runtime context leads to "Logic Hallucinations," where the AI guesses how a UI should behave, leading to a 70% failure rate in legacy rewrites.
• •Static analysis misses 90% of user workflows and ephemeral states.
• •Replay solves this by using Visual Reverse Engineering to capture real user recordings, converting them into documented React components with 70% average time savings.
• •Modernization should move from an 18-month manual cycle to a weeks-long automated flow using runtime-aware tools.

Why Context is the "Missing Link" in Legacy Modernization#

When we talk about legacy systems—the green screens of insurance giants, the Swing-based terminals in healthcare, or the Delphi applications in manufacturing—we are looking at software where documentation is nonexistent 67% of the time. In these environments, the "source of truth" isn't the code; it’s the behavior of the application during runtime.

Standard generative AI models operate on pattern matching. They see a "Login" screen and generate a standard React

component. However, they miss the fact that the legacy system requires a specific sequence of keystrokes to trigger a hidden validation field, or that the "Submit" button remains disabled until a specific background socket connection is established. Without this context, you aren't modernizing; you're just guessing.

Video-to-code is the process of recording real-world user interactions with a legacy application and using those visual and behavioral signals to generate functional, documented React components.

According to Replay's analysis, manual migration of a single complex enterprise screen takes an average of 40 hours. When using Replay, that time is slashed to 4 hours because the AI isn't guessing—it’s observing the actual runtime behavior.

The Hidden Dangers Using Generative Models for UI Logic#

1. The Logic Hallucination Trap#

The most pervasive of the hidden dangers using generative AI is the hallucination of business logic. An LLM might see a dropdown and assume it’s a standard HTML

element. In reality, that dropdown might trigger a complex state change across three other components. If the AI doesn't see the "Flow" of data, it will write clean-looking React code that is functionally useless.

2. The Documentation Gap#

67% of legacy systems lack documentation. When you use a "blind" AI, you are essentially asking it to write documentation for code it doesn't understand. This leads to a "Black Box" React component. You might get the UI, but you lose the "Why" behind the implementation.

3. Accessibility and ARIA Mismatches#

Legacy systems often have highly customized (and often non-standard) accessibility patterns. Generative AI often defaults to "best guess" ARIA labels which may conflict with the actual required behavior of the system, leading to compliance risks in regulated industries like Finance or Government.

Comparing Approaches: Blind GenAI vs. Visual Reverse Engineering#

Technical Deep Dive: The Cost of Missing State#

Let's look at a practical example. Suppose we are modernizing a legacy claims processing screen. A standard AI might see the following UI and generate a simple React component.

The "Blind" AI Output (Dangerous)#

This code looks correct at first glance, but it ignores the legacy system's requirement that the

must be validated against an external legacy API before the field is even enabled.

typescript
// Generated by a "Blind" AI - Missing Runtime Context
import React, { useState } from 'react';

const ClaimsForm = () => {
  const [amount, setAmount] = useState(0);
  const [type, setType] = useState('auto');

  const handleSubmit = () => {
    // AI guesses the submission logic
    console.log("Submitting:", { amount, type });
  };

  return (
    <form onSubmit={handleSubmit}>
      <select value={type} onChange={(e) => setType(e.target.value)}>
        <option value="auto">Auto</option>
        <option value="home">Home</option>
      </select>
      <input 
        type="number" 
        value={amount} 
        onChange={(e) => setAmount(Number(e.target.value))} 
      />
      <button type="submit">Submit Claim</button>
    </form>
  );
};

The Replay-Informed Component (Robust)#

By using Replay's Flows, the system captures the actual sequence: User selects type -> System hits

-> UI enables amount field. Replay's AI Automation Suite understands these dependencies because it saw them happen in the recording.

typescript
// Generated via Replay Visual Reverse Engineering
import React, { useEffect } from 'react';
import { useClaimsLogic } from './hooks/useClaimsLogic'; // Logic extracted from runtime
import { Button, Select, Input } from '@your-org/design-system';

export const ClaimsForm = () => {
  const { 
    state, 
    actions, 
    validationStatus 
  } = useClaimsLogic(); // Replay identified this state machine from the recording

  return (
    <div className="p-6 bg-white rounded-lg shadow-md">
      <h2 className="text-xl font-bold mb-4">Submit Claim</h2>
      <Select
        label="Policy Type"
        options={state.policyOptions}
        value={state.selectedType}
        onChange={actions.handleTypeChange}
      />
      
      {/* Replay observed that this field is conditional based on legacy API response */}
      <Input
        label="Claim Amount"
        type="number"
        disabled={!validationStatus.isPolicyValid}
        value={state.amount}
        onChange={actions.handleAmountChange}
        error={validationStatus.error}
      />

      <Button 
        variant="primary" 
        onClick={actions.submit}
        loading={state.isSubmitting}
      >
        Submit to Mainframe
      </Button>
    </div>
  );
};

Industry experts recommend that for any system involving complex transactional state, developers must prioritize behavioral capture over static code conversion. You can read more about this in our guide on Component Library Architecture.

Technical Debt and the Hidden Dangers Using Generative AI for Legacy UI#

The $3.6 trillion technical debt crisis isn't just about old code; it's about "zombie logic"—code that no one understands but everyone is afraid to change. When you use generative AI without context, you are effectively "re-skinning" the zombie.

According to Replay's analysis, 70% of legacy rewrites fail or exceed their timeline precisely because the transition from "Old System Logic" to "New React Components" is handled manually or with insufficient data. The average enterprise rewrite takes 18 months. By the time the project is 50% complete, the requirements have changed, and the "blind" AI code has already become legacy code itself.

Replay changes this trajectory. By focusing on the Library (Design System) and Flows (Architecture), Replay allows teams to move from 18-24 months to just days or weeks.

Why Regulated Industries Cannot Risk "Blind" AI#

For Financial Services, Healthcare, and Government, the hidden dangers using generative AI extend into the realm of security and compliance.

• •Data Sovereignty: Sending legacy UI screenshots to public LLMs can leak PII (Personally Identifiable Information) or sensitive internal system architectures.
• •Auditability: In a regulated environment, you must be able to prove why a component behaves the way it does. Replay provides a clear lineage from the recording to the code.
• •On-Premise Requirements: Many generative tools are cloud-only. Replay offers On-Premise availability and is SOC2 and HIPAA-ready, ensuring that your modernization journey doesn't become a security liability.

The Replay Workflow: From Recording to React#

The process of avoiding the hidden dangers using generative AI involves four key pillars:

1. •Record (Capture Context): A developer or subject matter expert records the legacy workflow. This isn't just a video; it's a data-rich capture of the UI's behavior.
2. •Analyze (Replay Blueprints): Replay's AI analyzes the recording, identifying patterns, components, and state transitions. It maps the "Flow" of the application.
3. •Generate (The Library): Instead of a monolithic block of code, Replay generates a structured Design System and Component Library. Building a Design System from Legacy is a critical step in ensuring long-term maintainability.
4. •Refine (The Editor): Developers use the Replay Blueprint editor to fine-tune the generated React code, ensuring it perfectly matches the enterprise's coding standards.

Avoiding the "Manual Screen" Trap#

The traditional way to modernize involves a developer sitting with a legacy app on one screen and a code editor on the other. They spend 40 hours per screen manually mapping buttons, inputs, and logic.

If an enterprise has 500 screens, that's 20,000 man-hours. At a conservative $100/hour, that’s a $2 million investment just for the UI layer, with a high probability of human error.

By using Replay, that same project drops to 2,000 hours. The "Visual Reverse Engineering" approach doesn't just save money; it eliminates the risk of missing critical "hidden" logic that a human might overlook during a manual port.

Case Study: Telecom Modernization#

A major telecom provider had a 20-year-old billing system. Previous attempts to modernize using standard GenAI failed because the AI couldn't account for the complex "if-then" logic required for regional tax calculations visible only during the final step of a multi-page flow. Using Replay, they recorded the workflow, and the platform automatically identified the conditional rendering logic, saving them 14 months of development time.

Frequently Asked Questions#

What are the main hidden dangers using generative AI for React conversion?#

The primary dangers include "Logic Hallucinations" (where AI guesses functionality), missing edge cases in state management, and the creation of non-compliant or inaccessible UI components. Without runtime context, the AI lacks the data necessary to understand how a component should actually function in a real-world enterprise environment.

How does Replay differ from tools like Copilot or ChatGPT?#

While Copilot and ChatGPT are excellent for general coding assistance, they lack "Visual Context." They cannot see your legacy application in action. Replay is a Visual Reverse Engineering platform that uses video recordings of your actual legacy workflows to generate documented, context-aware React components and design systems.

Is Replay secure for highly regulated industries?#

Yes. Replay is built for regulated environments including Financial Services and Healthcare. It is SOC2 and HIPAA-ready, and for organizations with strict data sovereignty requirements, an On-Premise deployment option is available. This ensures your legacy data never leaves your secure perimeter.

How much time can I really save with Visual Reverse Engineering?#

On average, Replay users see a 70% time savings. This means a manual process that typically takes 40 hours per screen can be completed in approximately 4 hours. This shifts the enterprise modernization timeline from years to weeks.

Does Replay generate a full Design System?#

Yes. One of Replay's core features is the Library, which automatically extracts and documents a consistent Design System from your legacy recordings. This ensures that your new React application is not just a collection of components, but a cohesive, scalable ecosystem. You can learn more about Design System automation here.

Conclusion: Context is the Only Way Forward#

The era of "blind" modernization is over. The risks—hallucinated logic, security vulnerabilities, and the massive cost of failed rewrites—are too high for the modern enterprise. To truly solve the $3.6 trillion technical debt problem, we must move beyond static code analysis and embrace runtime UI context.

By leveraging Visual Reverse Engineering, platforms like Replay allow architects to bridge the gap between the legacy past and the React future with precision, speed, and security. Don't let your modernization project become another statistic in the 70% failure rate.

Ready to modernize without rewriting? Book a pilot with Replay