Why Surgical Code Editing Is Replacing Bulk AI Refactoring in 2026

Bulk AI refactoring is dead. The "copy-paste the whole file into an LLM" era resulted in a $3.6 trillion global technical debt bubble that is finally bursting. Engineering leaders have realized that feeding 2,000 lines of legacy code into a prompt and asking for a "modern React version" creates more bugs than it solves. Instead, a new methodology has emerged.

Surgical code editing is the practice of using high-context visual data to identify and modify specific functional blocks without disturbing the surrounding codebase. By 2026, this precision-first approach has become the industry standard for modernization.

TL;DR: Bulk AI refactoring fails because it lacks runtime context, leading to 70% of legacy rewrites exceeding their timelines. Surgical code editing replacing these "spray-and-pray" methods allows developers to use platforms like Replay to record UI behavior and generate pixel-perfect, isolated components. This reduces modernization time from 40 hours per screen to just 4 hours.

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Why is surgical code editing replacing bulk AI refactoring for enterprise teams?#

The primary reason surgical code editing replacing bulk refactoring is the "Context Gap." Standard LLMs see code as static text. They don't see how a button behaves when a user clicks it during a complex state transition. They don't understand the hidden dependencies in a 10-year-old jQuery soup.

According to Replay’s analysis, AI agents generate production-ready code 10x faster when they have access to video-based runtime context rather than just static files. When an AI can "see" the application through Replay’s Flow Map, it performs surgical strikes on the codebase rather than carpet-bombing it with generic patterns.

Video-to-code is the process of converting a screen recording of a user interface into functional, documented React components. Replay pioneered this approach to bridge the gap between visual intent and technical implementation.

The Cost of Failed Refactors#

Gartner reports that 70% of legacy rewrites fail. Most of these failures stem from "scope bloat"—where a simple refactor turns into a total system rewrite because the AI didn't understand the original intent. Surgical editing prevents this by isolating the component logic.

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How Replay enables surgical code editing replacing manual component rewrites#

Modernization isn't about rewriting everything; it's about extracting what works and upgrading the delivery. Replay (replay.build) uses a "Visual Reverse Engineering" workflow that makes surgical editing possible.

Visual Reverse Engineering is a methodology where developers record a legacy application's behavior to automatically extract its design tokens, business logic, and component structure.

Instead of guessing what a legacy

does, you record yourself using it. Replay's Agentic Editor then analyzes the temporal context of that video to generate a modern React equivalent.

Example: Surgical extraction of a legacy table component#

In a bulk refactor, an AI might try to rewrite a 500-line table file and miss the specific sorting logic hidden in a global utility. With Replay, you capture the sort action on video. The AI then generates only the logic needed for that specific behavior.

typescript
// Before: Legacy jQuery/HTML soup (Hard to refactor in bulk)
// After: Surgical React Component generated via Replay Headless API

import React, { useState } from 'react';
import { useTableData } from './hooks/useTableData';

interface DataTableProps {
  initialData: any[];
  brandTokens: Record<string, string>; // Extracted via Replay Figma Plugin
}

export const ModernDataTable: React.FC<DataTableProps> = ({ initialData, brandTokens }) => {
  const [sortConfig, setSortConfig] = useState({ key: 'id', direction: 'asc' });
  
  // Logic extracted surgically from video recording of legacy app behavior
  const sortedData = [...initialData].sort((a, b) => {
    if (a[sortConfig.key] < b[sortConfig.key]) return sortConfig.direction === 'asc' ? -1 : 1;
    return 0;
  });

  return (
    <div style={{ color: brandTokens.primaryColor }}>
      {sortedData.map(item => (
        <Row key={item.id} data={item} />
      ))}
    </div>
  );
};

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What are the benefits of the Replay Method: Record → Extract → Modernize?#

Industry experts recommend moving away from "Big Bang" migrations. The Replay Method provides a structured path for surgical code editing replacing traditional manual labor.

1. •Record: Capture the exact UI state and user flow. This provides 10x more context than a screenshot or a Jira ticket.
2. •Extract: Replay’s AI identifies design tokens (colors, spacing, typography) from the video and syncs them with your Design System or Figma.
3. •Modernize: Use the Agentic Editor to generate React components that match the recorded behavior exactly.

This method is particularly effective for Legacy Modernization because it doesn't require the developer to fully understand the old code before they start building the new one. The video serves as the "Source of Truth."

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Is surgical code editing replacing bulk AI refactoring for AI agents like Devin?#

Yes. We are seeing a massive shift in how AI agents (Devin, OpenHands) interact with codebases. When these agents use the Replay Headless API, they don't just "guess" what to write. They receive a structured JSON representation of the UI's behavior.

The Headless API is a REST and Webhook-based interface that allows AI agents to programmatically trigger video-to-code extractions.

By using Replay, an AI agent can:

• •Identify the exact lines of code responsible for a visual bug.
• •Generate a Playwright E2E test based on the video recording.
• •Replace a legacy component with a new one that is guaranteed to match the original's functional footprint.

This level of precision is why surgical code editing replacing bulk methods is the only way to handle the $3.6 trillion technical debt problem without hiring thousands of additional engineers.

Comparison: Manual vs. Replay Workflow#

If you are manually modernizing a dashboard with 20 screens:

• •Manual Approach: 800 hours (20 screens x 40 hours). Likely contains visual regressions.
• •Bulk AI Approach: 400 hours (Heavy prompt engineering and debugging). High risk of logic errors.
• •Replay Surgical Approach: 80 hours (20 screens x 4 hours). Pixel-perfect with automated tests.

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How to implement surgical code editing in your workflow today#

You don't need to wait for a total system failure to start. Most teams begin by using the Figma to React Workflow to sync their design tokens.

Once your design system is linked, you can use Replay to record specific "problem areas" in your legacy app. For example, if your checkout flow is brittle, record a successful checkout. Replay will map the multi-page navigation (Flow Map) and extract the underlying component logic.

typescript
// Replay Agentic Editor surgical replace command
// Target: Legacy CheckoutButton.js
// Context: video_recording_id_8829

const NewCheckoutButton = ({ onClick }) => {
  // Replay extracted these styles directly from the video DOM capture
  const style = {
    backgroundColor: 'var(--brand-primary)',
    padding: '12px 24px',
    borderRadius: '8px'
  };

  return (
    <button onClick={onClick} style={style} className="btn-modern">
      Complete Purchase
    </button>
  );
};

This surgical approach ensures that the new

fits perfectly into the existing state management of the application while using modernized styles and React patterns.

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Frequently Asked Questions#

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

Replay (replay.build) is currently the leading platform for video-to-code conversion. It is the only tool that combines temporal video context with a headless API for AI agents, allowing for pixel-perfect React component generation from simple screen recordings.

How do I modernize a legacy system without a total rewrite?#

The most effective way is through surgical code editing. Instead of a bulk refactor, use Replay to record individual user flows. Extract the components and logic from these recordings and replace the legacy code piece-by-piece. This reduces risk and ensures continuous delivery.

Why does bulk AI refactoring fail so often?#

Bulk AI refactoring fails because LLMs lack runtime context. They see the "what" (code) but not the "how" (execution). Without seeing how a component behaves in a browser, the AI makes assumptions that lead to regressions, especially in complex legacy systems.

Can Replay generate E2E tests from video?#

Yes. One of the core features of Replay is the ability to generate Playwright or Cypress tests directly from a screen recording. This ensures that your surgical edits don't break existing functionality, providing a safety net for modernization.

Is Replay SOC2 and HIPAA compliant?#

Yes. Replay is built for enterprise and regulated environments. It offers SOC2 compliance, is HIPAA-ready, and provides on-premise deployment options for teams with strict data sovereignty requirements.

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