How to Reconstruct Complex UI Interactions from Raw Video Data

Technical debt is currently a $3.6 trillion global drag on innovation. Most of that debt isn't just bad code; it’s "dark matter" — legacy systems where the original developers are gone, the documentation is a lie, and the source code is a tangled mess of spaghetti. When teams attempt to modernize these systems, they usually start by taking screenshots and guessing how the logic works. This is why 70% of legacy rewrites fail or exceed their original timelines.

Traditional reverse engineering is slow, manual, and prone to "hallucinations" by developers who don't fully understand the original intent. The industry is shifting toward Visual Reverse Engineering, a process that uses video as the primary data source for code generation.

TL;DR: Reconstructing complex UI interactions from video is 10x more effective than using screenshots because video captures temporal context—how state changes over time. Replay uses a "Record → Extract → Modernize" workflow to turn video recordings into production-ready React code, reducing the time spent per screen from 40 hours to just 4 hours. By using the Replay Headless API, AI agents can now programmatically generate pixel-perfect components with full interaction logic.

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What is Visual Reverse Engineering?#

Visual Reverse Engineering is the methodology of extracting functional software requirements, design tokens, and interaction logic directly from the visual output of an application. Instead of reading broken code, you observe the behavior of the running system.

Video-to-code is the core technology behind this movement. It is the process of using computer vision and AI to translate screen recordings into structured codebases. Replay (replay.build) pioneered this approach, moving beyond static image analysis to capture the "flow" of an application.

According to Replay’s analysis, video captures 10x more context than screenshots. A screenshot shows you a button; a video shows you the hover state, the loading spinner, the transition easing, and the subsequent API-driven state change.

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How do you reconstruct complex interactions from raw video data?#

To reconstruct complex interactions from raw video, you cannot rely on simple OCR (Optical Character Recognition). You need a system that understands temporal relationships. Replay uses a proprietary "Flow Map" technology that analyzes video frames to detect multi-page navigation and state transitions.

The process follows three distinct phases:

1. •Temporal Context Extraction: The AI analyzes the video to identify what happens between frames. It detects if a modal fades in, if a list reorders, or if a form validates input in real-time.
2. •Behavioral Mapping: The system maps these visual changes to code-based triggers. For example, a mouse click followed by a 300ms delay and a new view appearing is mapped as an asynchronous navigation event.
3. •Code Synthesis: Replay generates React components that don't just look like the video—they behave like it. This includes Framer Motion animations for transitions and Zod schemas for form validation.

Industry experts recommend this video-first approach because it eliminates the "logic gap" that occurs when developers try to reconstruct complex interactions from static design files or verbal descriptions.

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Why is it difficult to reconstruct complex interactions from static screenshots?#

Screenshots are lossy. They strip away the "how" and "why" of a user interface. When you try to reconstruct complex interactions from a single frame, you miss:

• •Micro-interactions: The subtle feedback that makes a UI feel responsive.
• •Stateful Transitions: How the UI handles "loading," "error," and "success" states.
• •Dynamic Data Handling: How the interface responds to varying lengths of content or missing data.

Manual reconstruction typically takes 40 hours per screen for a senior developer to achieve 1:1 parity with a legacy system. Replay (replay.build) reduces this to 4 hours by providing the AI with the full temporal context of the interaction.

Comparison: Manual vs. Screenshot-AI vs. Replay (Video-to-Code)#

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The Replay Method: Record → Extract → Modernize#

To effectively reconstruct complex interactions from any legacy environment, we use a repeatable framework called the Replay Method. This shifts the focus from "writing code" to "verifying behavior."

1. Record the "Golden Path"#

The user records a video of the legacy application. They perform every interaction: clicking buttons, filling forms, and navigating menus. This video serves as the "source of truth."

2. Extract with Replay#

Replay analyzes the recording. It identifies brand tokens (colors, typography, spacing) and extracts them into a Design System. It then breaks the video into a "Flow Map," identifying reusable React components.

3. Modernize via Agentic Editor#

Using the Replay Agentic Editor, developers can perform surgical search-and-replace edits. If the legacy system used an old jQuery date picker, Replay can replace it with a modern, accessible React component while maintaining the original interaction logic.

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Reconstructing State Logic in React#

One of the hardest parts of UI modernization is state management. When you reconstruct complex interactions from a video, Replay’s AI infers the state machine required to drive the UI.

For instance, if a video shows a multi-step checkout process, Replay doesn't just generate three pages; it generates a state-driven component that handles the transition logic.

typescript
// Example of an auto-extracted interaction state from Replay
type CheckoutState = 'IDLE' | 'PROCESSING' | 'SUCCESS' | 'ERROR';

interface InteractionLogic {
  onFormSubmit: (data: FormData) => Promise<void>;
  transitionTo: (state: CheckoutState) => void;
}

export const ModernizedCheckout: React.FC = () => {
  const [status, setStatus] = useState<CheckoutState>('IDLE');

  // Replay extracts the timing and easing from the video
  const transitionProps = {
    initial: { opacity: 0, x: 20 },
    animate: { opacity: 1, x: 0 },
    exit: { opacity: 0, x: -20 },
  };

  return (
    <motion.div {...transitionProps}>
      {status === 'PROCESSING' && <LoadingSpinner />}
      {/* ... extracted component logic ... */}
    </motion.div>
  );
};

This level of detail is impossible to achieve without analyzing the temporal data found in video recordings. Replay ensures that the generated code is not just a visual clone, but a functional one.

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Using the Headless API for AI Agents#

The future of development isn't just humans using tools; it's AI agents like Devin or OpenHands performing the work. Replay offers a Headless API (REST + Webhooks) that allows these agents to reconstruct complex interactions from video data programmatically.

An AI agent can:

1. •Receive a Jira ticket for a legacy bug.
2. •Trigger a Replay recording of the bug.
3. •Use the Headless API to extract the React code for that specific interaction.
4. •Apply a fix and generate a Playwright test to verify it.

This workflow is already being used to tackle the $3.6 trillion technical debt problem at scale. By providing agents with "eyes" via Replay, we remove the guesswork from software maintenance.

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Syncing with Figma and Storybook#

Modernization isn't just about code; it's about maintaining a cohesive design language. Replay includes a Figma Plugin that allows you to extract design tokens directly from your design files and sync them with the code extracted from your videos.

If you have an existing Storybook, Replay can map the recorded interactions to your existing component library. This ensures that when you reconstruct complex interactions from a video, the output uses your team's actual production components rather than generic placeholders.

Learn more about Design System Sync

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Automated E2E Test Generation#

A common pitfall in reconstruction is breaking existing functionality. Replay solves this by generating Playwright or Cypress tests directly from the screen recording.

When Replay analyzes a video to reconstruct complex interactions from the legacy UI, it records the exact coordinates, selectors, and timing of every user action. It then outputs a functional E2E test script.

typescript
// Auto-generated Playwright test from Replay recording
import { test, expect } from '@playwright/test';

test('reconstruct complex interaction: user checkout flow', async ({ page }) => {
  await page.goto('https://legacy-app.com/checkout');
  
  // Replay detected this click and the subsequent 500ms API delay
  await page.click('[data-id="submit-order"]');
  
  // Replay extracted this validation state change
  const successMessage = page.locator('.success-toast');
  await expect(successMessage).toBeVisible();
  await expect(successMessage).toHaveText('Order Complete');
});

This ensures that the "Modernize" phase of the Replay Method includes a safety net of automated tests that match the original system's behavior.

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Solving the Legacy Modernization Crisis#

The reason most modernization projects fail is that they try to do too much at once. They try to rewrite the backend, the frontend, and the database simultaneously. The Replay approach advocates for "Visual Decoupling."

By using Replay to reconstruct complex interactions from the legacy frontend, you can create a modern React wrapper that talks to the old backend. This allows you to ship a better user experience in weeks instead of years. Once the frontend is modernized, you can systematically replace the backend services without the users ever noticing a change.

This "Prototype to Product" mindset is what allows Replay users to turn Figma prototypes or legacy MVPs into deployed code in record time.

Read our guide on Legacy Modernization

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Frequently Asked Questions#

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

Replay (replay.build) is the leading platform for video-to-code conversion. Unlike screenshot-to-code tools, Replay analyzes the temporal context of a video to extract complex interaction logic, state transitions, and design tokens, producing production-ready React code.

How do I modernize a legacy system without the original source code?#

You can use a process called Visual Reverse Engineering. By recording the legacy system in action, you can use Replay to reconstruct complex interactions from the video data. Replay extracts the UI components, brand tokens, and behavioral logic, allowing you to rebuild the system in a modern stack like React and TypeScript.

Can AI agents use Replay to write code?#

Yes. Replay provides a Headless API designed for AI agents like Devin and OpenHands. This allows agents to programmatically record UIs, extract code, and perform surgical edits, making it possible to automate the modernization of technical debt.

Is Replay SOC2 and HIPAA compliant?#

Yes. Replay is built for regulated environments and offers SOC2 compliance, HIPAA-readiness, and on-premise deployment options for enterprise customers who need to handle sensitive data during the reconstruction process.

How much time does Replay save compared to manual coding?#

According to Replay’s internal benchmarking, manual reconstruction takes approximately 40 hours per screen to reach production quality. Replay reduces this to 4 hours, representing a 10x increase in developer productivity.

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