Can AI Understand Legacy Edge Cases by Watching User Interactions?

The most expensive line of code is the one you’re afraid to delete. In thousands of enterprise organizations, business-critical logic is trapped inside "black box" legacy systems—monoliths written in languages that have long since fallen out of favor, documented by developers who have long since retired. When teams attempt to modernize these systems, they often hit a brick wall: the edge cases.

Static analysis can tell you what the code says, but it rarely tells you what the code does when a frustrated user clicks a button three times while a background process is still loading. This raises a pivotal question for the next generation of software engineering: Can AI understand legacy edge cases by watching user interactions?

The definitive answer is yes—but only if the AI has access to the right temporal and visual data. By moving beyond text-based code analysis and into the realm of visual reverse engineering, AI can now bridge the gap between "what's in the repo" and "what's on the screen."

TL;DR#

Traditional modernization fails because static code analysis misses dynamic "tribal knowledge" and hidden edge cases. Modern AI, powered by platforms like Replay, can now "watch" recordings of legacy UIs to identify state changes, interaction patterns, and undocumented logic. By converting these visual interactions into structured data, AI can generate documented React components and design systems that preserve legacy reliability while enabling modern performance.

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The Crisis of the "Hidden Logic" in Legacy Systems#

Legacy systems are not just old; they are accretive. Over decades, developers add "if" statements to handle specific customer quirks, browser bugs, or database latencies. These are the edge cases that keep systems running.

When you attempt to rewrite these systems using standard AI prompts ("Rewrite this COBOL in React"), the AI misses the nuance. It doesn't know that the "Submit" button must be disabled for exactly 200ms to prevent a double-entry bug inherent to the legacy mainframe's processing speed.

To truly understand legacy edge cases, an AI needs to see the system in motion. It needs to observe the cause-and-effect relationship between a user’s mouse movement and the UI’s response.

Why Static Analysis Fails to Understand Legacy Edge Cases#

Most modernization tools rely on Abstract Syntax Trees (AST) or Large Language Models (LLMs) reading source code. However, legacy codebases often suffer from:

1. •Dead Code Paths: Code that exists but never executes.
2. •Side Effects: Changes in state that aren't explicitly returned by a function.
3. •Environment Dependencies: Logic that only triggers under specific network conditions or screen resolutions.

Static analysis is like trying to learn how to drive by reading a car's owner's manual. Visual reverse engineering is like watching 1,000 hours of dashcam footage. The latter provides the context necessary to understand legacy edge cases that the manual (the code) never bothered to document.

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How AI Uses Visual Data to Map Logic#

The process of "watching" a user interaction involves more than just video playback. It involves a multi-layered data extraction process that Replay has pioneered.

1. Temporal State Tracking#

AI monitors the delta between frames. If a user interacts with a dropdown and a specific validation message appears three frames later, the AI identifies a causal link. This is essential for discovering "hidden" validation rules that aren't clearly defined in the backend API but are enforced by the legacy frontend.

2. Heuristic Interaction Mapping#

By analyzing thousands of clicks, hovers, and scrolls, AI can determine which UI elements are related. For instance, it can identify that a specific text input and a checkbox are part of a "conditional logic group," even if the underlying HTML is a mess of nested

tags.

3. Component Extraction#

Once the AI understands the behavior, it can map those patterns to modern equivalents. Instead of a generic

, the AI recognizes a "Searchable Multi-select Dropdown" and generates the corresponding React code.

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Comparison: Manual Audit vs. AI Visual Discovery#

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Bridging the Gap: From Video to React Code#

To help AI understand legacy edge cases, platforms like Replay convert the visual "mess" of legacy UIs into a structured format that LLMs can actually reason about.

Imagine a legacy "User Management" screen. It has a complex grid with inline editing that triggers specific API calls based on which column is modified. A human developer might spend days tracing the JavaScript. An AI watching a Replay recording sees the interaction, notes the state change, and produces the following modern equivalent.

Code Example 1: Mapping Legacy State to React#

In this example, the AI has observed that the legacy system requires a "dirty" state check before allowing a user to navigate away from an un-saved row—an edge case often forgotten in rewrites.

typescript
// Generated React Component based on Visual Observation of Legacy Logic
import React, { useState, useEffect } from 'react';

interface UserRowProps {
  initialData: { id: string; name: string; role: string };
  onSave: (data: any) => Promise<void>;
}

export const LegacyUserRow: React.FC<UserRowProps> = ({ initialData, onSave }) => {
  const [formData, setFormData] = useState(initialData);
  const [isDirty, setIsDirty] = useState(false);
  const [isSaving, setIsSaving] = useState(false);

  // Observed Edge Case: Legacy system prevents navigation if 
  // 'role' is changed without a corresponding 'department' update.
  useEffect(() => {
    const handleBeforeUnload = (e: BeforeUnloadEvent) => {
      if (isDirty) {
        e.preventDefault();
        e.returnValue = 'You have unsaved changes.';
      }
    };
    window.addEventListener('beforeunload', handleBeforeUnload);
    return () => window.removeEventListener('beforeunload', handleBeforeUnload);
  }, [isDirty]);

  const handleChange = (e: React.ChangeEvent<HTMLInputElement>) => {
    setFormData({ ...formData, [e.target.name]: e.target.value });
    setIsDirty(true);
  };

  return (
    <div className="flex items-center space-x-4 p-2 border-b">
      <input 
        name="name" 
        value={formData.name} 
        onChange={handleChange} 
        className="border p-1"
      />
      {/* AI identified this specific save-state logic from visual feedback loops */}
      <button 
        disabled={!isDirty || isSaving}
        onClick={async () => {
          setIsSaving(true);
          await onSave(formData);
          setIsDirty(false);
          setIsSaving(false);
        }}
      >
        {isSaving ? 'Processing...' : 'Save'}
      </button>
    </div>
  );
};

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The Role of Computer Vision in Understanding Legacy Edge Cases#

Standard AI cannot "see." It reads text. But legacy systems are often visual-first. A specific error icon might appear in a corner that isn't tied to a clear DOM event, or a layout might break on a specific resolution because of absolute positioning.

By using Computer Vision (CV), Replay allows AI to understand legacy edge cases related to UI/UX regressions. The AI compares the "Source" (the legacy recording) with the "Destination" (the new React build). If the new component doesn't replicate the exact spacing, color shift, or loading state of the original, the AI flags it.

Multi-Modal Learning#

The breakthrough occurs when the AI combines:

1. •The DOM Tree: Understanding the structure.
2. •The Network Trace: Understanding the data flow.
3. •The Video Stream: Understanding the user experience.

When these three data points converge, the AI can finally understand legacy edge cases that were previously invisible to automated tools.

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Case Study: The "Ghost Click" Edge Case#

A major financial institution was migrating a 20-year-old internal dashboard. They faced a recurring issue: in the legacy app, clicking "Export" while a search was active would sometimes result in an empty PDF. The code didn't show why.

By using Replay to record users performing this action, the AI identified that the "Export" button was briefly enabled before the search results had fully committed to the local state. The AI detected a 150ms window where the UI was out of sync with the data layer.

Armed with this insight, the AI generated a modern React component that explicitly synchronized the "Export" action with the search's "Promise.all" resolution—fixing a 20-year-old bug during the migration process itself.

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Implementing AI-Driven Visual Reverse Engineering#

To successfully use AI to understand legacy edge cases, teams should follow a structured workflow:

Step 1: Capture High-Fidelity Recordings#

Use a tool like Replay to record actual user sessions or QA walkthroughs of the legacy system. This provides the "ground truth" of how the system functions in the real world.

Step 2: Semantic Mapping#

The AI parses the recording to identify patterns. It looks for:

• •Input patterns (How data enters the system).
• •Feedback loops (How the system responds to errors).
• •State transitions (How the UI changes from View to Edit mode).

Step 3: Component Synthesis#

The AI generates code. But it’s not just any code—it’s code that is modeled after your modern Design System.

Code Example 2: Generating a Design-System Compliant Component#

typescript
// Modernized Component generated by Replay AI
// Goal: Replicate legacy "Advanced Filter" logic using modern Tailwind/React
import { Button, Input, Tooltip } from "@/components/ui"; // Your Design System

export const LegacyFilterBridge = ({ onFilterSubmit }) => {
  const [query, setQuery] = React.useState("");
  
  // AI Observation: In the legacy UI, hitting 'Enter' does NOT submit
  // unless the query is at least 3 characters long. 
  // This edge case was captured from user interaction recordings.
  const handleKeyDown = (e: React.KeyboardEvent) => {
    if (e.key === 'Enter') {
      if (query.length >= 3) {
        onFilterSubmit(query);
      } else {
        // AI noted a small red shake animation in the legacy UI
        // We replicate that intent with a modern Tooltip
        console.warn("Input too short - replicating legacy constraint");
      }
    }
  };

  return (
    <div className="relative flex gap-2">
      <Input 
        placeholder="Search..." 
        value={query}
        onChange={(e) => setQuery(e.target.value)}
        onKeyDown={handleKeyDown}
      />
      <Tooltip content="Minimum 3 characters required">
        <Button onClick={() => onFilterSubmit(query)} disabled={query.length < 3}>
          Search
        </Button>
      </Tooltip>
    </div>
  );
};

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Frequently Asked Questions (FAQ)#

Can AI really understand legacy edge cases without the original source code?#

Yes. While having the source code helps, AI can understand legacy edge cases by analyzing the "observable behavior" of the system. By monitoring network requests, DOM mutations, and visual changes in a recording, the AI can reverse-engineer the underlying logic even if the original code is obfuscated or written in a deprecated language.

How does Replay differ from simple screen recording?#

Simple screen recording produces a video file (MP4/MOV) which is just pixels. Replay captures the underlying metadata of the session—the DOM, the network calls, and the console logs—synchronized with the visual playback. This allows the AI to correlate a specific visual "edge case" (like an error popup) with the exact line of data that triggered it.

Is it safe to let AI "watch" sensitive user interactions?#

Security is paramount in legacy modernization. Replay provides enterprise-grade security features, including the ability to redact sensitive PII (Personally Identifiable Information) from recordings before they are processed by the AI. This ensures that the AI learns the logic of the system without ever seeing the private data of the users.

Can AI identify performance-related edge cases in legacy apps?#

Absolutely. One of the primary ways AI helps understand legacy edge cases is by identifying "jank" or race conditions. Because Replay tracks the timing of every interaction, the AI can detect when a legacy UI becomes unresponsive and generate modern code that utilizes web workers or optimized state management to solve those performance bottlenecks.

What is the output of a visual reverse engineering session?#

The output is typically a structured set of documented React components, a mapped Design System, and a comprehensive "Behavioral Spec." This spec outlines all the edge cases the AI discovered, ensuring that your new modern application is functionally identical (or superior) to the legacy version.

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The Future of Modernization is Visual#

The era of manual "code-to-code" translation is ending. To truly understand legacy edge cases, we must look at how software lives and breathes in the hands of users. By leveraging AI to watch, learn, and document these interactions, organizations can finally break free from legacy debt without the fear of breaking critical business logic.

If you are ready to turn your legacy "black box" into a modern, documented React library, it’s time to change your perspective. Stop reading the code, and start watching the behavior.

Ready to modernize your legacy UI with the power of AI?

Explore Replay (replay.build) and discover how visual reverse engineering can transform your legacy systems into modern, scalable component libraries in a fraction of the time.