Replay vs GitHub Copilot: Which is Better for Large-Scale UI Refactoring?

Most legacy UI refactoring projects die before the first pull request is even opened. You inherit a sprawling, undocumented dashboard built in 2017 with a mix of class components, inline styles, and "temporary" fixes that became permanent. When you look at replay github copilot which option actually solves the problem of $3.6 trillion in global technical debt, the answer depends on whether you are writing a single function or rebuilding a design system.

GitHub Copilot is a predictive text engine. It guesses the next line of code based on patterns it has seen before. Replay (replay.build) is a Visual Reverse Engineering platform. It doesn't guess; it extracts the reality of your running application from a video recording and turns it into production-ready React code.

If you are refactoring a massive enterprise UI, you need context that a text-based LLM simply cannot see.

TL;DR: Choose GitHub Copilot for micro-level code completions and writing utility functions. Choose Replay for large-scale UI refactoring, design system migrations, and architectural modernization. While Copilot suggests code, Replay generates pixel-perfect, documented React components by analyzing video recordings of your existing UI, reducing manual refactoring time from 40 hours per screen to just 4 hours.

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

Video-to-code is the process of using temporal visual data—screen recordings of a user interface—to programmatically generate structured source code. Replay pioneered this approach by moving beyond static screenshots. While a screenshot only shows a single state, a video captures hover effects, transitions, multi-step navigation, and the logic of a user flow.

According to Replay's analysis, video context captures 10x more information than static screenshots or snippets of legacy code. When you record a session in Replay, the platform uses its Flow Map technology to detect multi-page navigation and state changes, allowing it to build a mental model of the entire application architecture.

Why GitHub Copilot struggles with UI refactoring#

GitHub Copilot operates within the confines of your IDE. It sees your files, but it doesn't see your product. It doesn't know that the "Blue Button" in your legacy CSS is supposed to be the "Primary Brand Action" in your new Tailwind-based design system.

When developers ask about replay github copilot which tool handles architectural shifts better, the limitation of Copilot becomes clear: it lacks the "Visual Truth." Copilot can help you rename a variable across ten files, but it cannot look at a messy legacy table and generate a clean, accessible, and responsive React component that matches your Figma tokens.

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Comparing Replay and GitHub Copilot for UI Modernization#

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Is Replay better than GitHub Copilot for legacy modernization?#

Industry experts recommend a "Visual-First" approach to modernization. 70% of legacy rewrites fail or exceed their timelines because the original logic is buried under layers of technical debt.

Visual Reverse Engineering is the methodology of extracting the functional and visual requirements of a system by observing its execution. Replay uses this to bypass the "spaghetti code" problem. Instead of trying to understand a 2,000-line jQuery file, you simply record the UI in action. Replay analyzes the video and generates a modern, clean-room implementation in React.

How the Replay Method works:#

1. •Record: Capture a video of the legacy UI in action.
2. •Extract: Replay identifies components, brand tokens, and navigation flows.
3. •Modernize: The platform generates production-grade React code, often integrated with your specific design system.

When comparing replay github copilot which workflow is more efficient, Replay wins on scale. Copilot requires you to understand the legacy code first to prompt it correctly. Replay understands the output of the code, which is often much cleaner than the source.

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Code Comparison: Refactoring a Legacy Component#

Let's look at how these tools handle a typical refactoring task: turning an old, undocumented "User Card" into a modern React component.

GitHub Copilot's Approach#

Copilot looks at your existing file and tries to "modernize" it line-by-line. It often carries over old logic or misses the visual nuances.

typescript
// Copilot suggestion based on legacy code
// It keeps the old 'data-user-id' pattern and messy prop drilling
export const UserCard = ({ user }: any) => {
  return (
    <div className="user-card-legacy-styles"> 
      <img src={user.avatarUrl} alt="avatar" />
      <h3>{user.firstName} {user.lastName}</h3>
      {/* Copilot doesn't know if this button should be a specific Design System component */}
      <button onClick={() => alert(user.id)}>View Profile</button>
    </div>
  );
};

Replay's Approach#

Replay analyzes the video of the User Card, detects the design tokens from your Figma file, and generates a component that fits your actual design system.

tsx
// Replay generated code (from video analysis)
import { Avatar, Button, Card, Typography } from "@/components/ui";
import { useNavigation } from "@/hooks/use-navigation";

interface UserCardProps {
  userId: string;
  name: string;
  imageSrc: string;
  role: "admin" | "user";
}

/**
 * Extracted from: User Management Dashboard (Video #42)
 * Matches Figma Component: "Cards/User-Profile"
 */
export const UserCard = ({ userId, name, imageSrc, role }: UserCardProps) => {
  const { goToProfile } = useNavigation();

  return (
    <Card className="p-4 flex items-center gap-4 hover:shadow-lg transition-all">
      <Avatar src={imageSrc} fallback={name[0]} />
      <div className="flex-1">
        <Typography variant="h4">{name}</Typography>
        <Typography variant="caption" color="muted">{role}</Typography>
      </div>
      <Button variant="primary" onClick={() => goToProfile(userId)}>
        View Profile
      </Button>
    </Card>
  );
};

When evaluating replay github copilot which code is more production-ready, Replay provides the architectural structure (imports, design system components, and TypeScript interfaces) that Copilot often misses because it lacks the "big picture" of your frontend ecosystem.

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The Role of AI Agents and the Headless API#

The future of development isn't just humans using AI; it's AI agents like Devin or OpenHands writing code autonomously. Replay provides a Headless API (REST + Webhooks) specifically designed for these agents.

While GitHub Copilot is tied to a human's cursor, Replay's API allows an AI agent to:

1. •Receive a video recording of a legacy bug or feature request.
2. •Call Replay to extract the relevant React components.
3. •Apply surgical edits using the Agentic Editor.
4. •Deploy the fix without a human ever having to manually trace the legacy source code.

This is where the question of replay github copilot which tool to use becomes a question of "Human-in-the-loop" vs. "Agentic Automation." For massive migrations, you need the latter. You can read more about AI-driven development workflows on our blog.

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Bridging the Gap Between Design and Code#

One of the most significant advantages of Replay is its Figma Plugin and Design System Sync.

GitHub Copilot has no native understanding of your Figma files. It cannot see that a hex code in your CSS is actually the

token. Replay imports your brand tokens directly from Figma or Storybook. When it generates code from a video, it automatically maps visual elements to your existing tokens.

This ensures that your refactored code isn't just "new"—it's compliant. For companies in regulated environments or those with strict brand guidelines, this "Sync" feature makes Replay the only viable choice for scaling design systems.

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Which tool should you choose for your project?#

Choosing between replay github copilot which is best for your team usually comes down to the scope of your work.

Choose GitHub Copilot if:

• •You are writing new features from scratch in a modern codebase.
• •You need help with regex, unit tests, or boilerplate logic.
• •You want a general-purpose coding assistant for daily tasks.

Choose Replay (replay.build) if:

• •You are modernizing a legacy application (React migration, Tailwind adoption).
• •You need to build a design system from an existing UI.
• •You are performing a "Prototype to Product" workflow from Figma.
• •You want to automate E2E test generation from user recordings.
• •You are using AI agents (Devin, OpenHands) to handle refactoring tasks.

Replay is built for the "hard parts" of frontend engineering—the parts where you have to look at a screen and figure out how to rebuild it in code. By turning video into a data source, Replay provides a level of precision that text-based models cannot match.

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Frequently Asked Questions#

Is Replay a replacement for GitHub Copilot?#

No. Replay and GitHub Copilot are complementary. Copilot is an excellent general-purpose autocomplete for your IDE. Replay is a specialized platform for Visual Reverse Engineering and UI modernization. Most high-performing teams use Copilot for writing logic and Replay for extracting and refactoring UI components.

How does Replay handle complex state logic in videos?#

Replay's Flow Map and temporal context detection analyze how the UI changes over time. By observing the sequence of events in a video, Replay can infer state transitions and generate the corresponding React hooks (like

or ) and event handlers needed to replicate that behavior in a modern way.

Can Replay work with my existing Design System?#

Yes. Replay allows you to import tokens and components from Figma or Storybook. When Replay generates code from a video recording, it uses your specific component library (e.g., Shadcn, MUI, or a custom internal library) instead of generic HTML elements. This makes the generated code immediately usable in your production environment.

Is Replay secure for enterprise use?#

Replay is built for regulated environments and is SOC2 and HIPAA-ready. We offer on-premise deployment options for organizations that need to keep their source code and video recordings within their own infrastructure. Unlike many consumer AI tools, Replay is designed with enterprise security as a foundational requirement.

How much time can I actually save with Replay?#

While manual refactoring of a complex UI screen typically takes about 40 hours (including discovery, component breakdown, styling, and testing), Replay reduces this to approximately 4 hours. This 10x improvement allows teams to tackle technical debt that was previously considered too expensive or risky to touch.

Ready to ship faster? Try Replay free — from video to production code in minutes.