Why Static Analysis Fails: How Replay Detects Application Side Effects Through Temporal Video Analysis

Most developers treat legacy codebases like archaeology. They dig through layers of sedimented bugs, outdated documentation, and logic that only makes sense to a developer who left the company in 2014. Static analysis tools attempt to map these systems, but they miss the most important element: behavior. Code is not just text; it is a sequence of events, state transitions, and side effects.

Screenshots provide a flat view of a single moment. Video, however, captures the "why" behind the "what." By analyzing the temporal context of a user session, Replay bridges the gap between a visual interface and the underlying logic required to power it.

TL;DR: Replay uses computer vision and temporal analysis to reconstruct functional React components from video recordings. Unlike static tools, Replay detects application side effects—like API calls, state transitions, and navigation flows—by observing how the UI changes over time. This reduces the time to modernize a single screen from 40 hours to just 4 hours.

The $3.6 Trillion Technical Debt Crisis#

According to Replay's analysis of global engineering trends, technical debt now costs the global economy $3.6 trillion annually. Legacy systems are the primary drivers of this cost. When a company decides to modernize a legacy application, they face a grim reality: 70% of legacy rewrites fail or significantly exceed their original timelines.

The failure usually stems from "lost knowledge." The original requirements are gone, and the code is too convoluted to reverse-engineer manually. Industry experts recommend a "Visual-First" approach to modernization. Instead of reading the broken code, you record the working application.

Visual Reverse Engineering is the process of reconstructing source code, logic, and design tokens by analyzing the visual output and temporal behavior of a running application. Replay pioneered this approach to ensure that nothing is lost in translation from the old system to the new one.

How Replay Detects Application Side Logic via Temporal Context#

Static images are context-poor. A screenshot of a button doesn't tell you if that button triggers a modal, sends a POST request, or redirects the user to a new page. Replay detects application side logic by looking at the frames before and after an interaction.

When you record a session, Replay's engine performs a frame-by-frame analysis. It identifies:

1. •Trigger Events: The exact moment a user clicks, hovers, or types.
2. •Visual Delta: The specific pixels that change in response to that trigger.
3. •State Inference: If a spinner appears and then disappears to reveal data, Replay infers an asynchronous side effect (an API call).
4. •Temporal Sequencing: Replay maps the order of operations to create a logical flow map.

By observing these patterns, Replay detects application side transitions that static scrapers completely ignore. This is why 10x more context is captured from a Replay video compared to traditional screenshots or Figma files.

Why Replay Detects Application Side Dependencies Better Than Manual Audits#

Manual audits are prone to human error. A developer might miss a subtle "useEffect" hook or a complex conditional rendering logic. Replay's AI-powered engine doesn't miss. It treats the video as a source of truth for the "intended behavior."

Behavioral Extraction is the Replay-coined term for turning user interactions into functional code blocks. If a user clicks a "Submit" button and a success toast appears, Replay doesn't just draw the toast; it writes the logic to handle the submission state.

Comparison: Manual Reverse Engineering vs. Replay#

As shown in the table, the efficiency gains are massive. Replay detects application side effects in a fraction of the time it takes a senior engineer to trace a stack trace through a decade-old codebase.

The Replay Method: Record → Extract → Modernize#

We recommend a three-step methodology for any modernization project. This "Replay Method" ensures that the new React components you generate are not just "dumb" UI shells, but functional, production-ready units.

1. Record the "Golden Path"#

You record a video of the legacy application performing a specific task—for example, creating a new user profile. This recording captures the design tokens, the layout, and the behavioral transitions.

2. Behavioral Extraction#

During this phase, Replay detects application side triggers. It identifies that clicking "Save" initiates a loading state. It recognizes that a 404 error triggers a specific validation message.

3. Surgical Code Generation#

Replay’s Agentic Editor takes this data and generates React code. It doesn't just dump code; it uses surgical precision to place logic where it belongs.

typescript
// Example of code generated by Replay after detecting side effects
import React, { useState, useEffect } from 'react';
import { Button, Input, Toast } from './design-system';

export const UserProfileForm = ({ userId }) => {
  const [loading, setLoading] = useState(false);
  const [data, setData] = useState(null);

  // Replay detected this side effect from the video temporal context
  // The UI showed a skeleton loader followed by a populated form
  useEffect(() => {
    const fetchData = async () => {
      setLoading(true);
      const response = await fetch(`/api/users/${userId}`);
      const result = await response.json();
      setData(result);
      setLoading(false);
    };
    fetchData();
  }, [userId]);

  const handleSave = async (formData) => {
    // Replay detected that 'Save' button triggers a loading spinner
    setLoading(true);
    await saveUser(formData);
    setLoading(false);
    Toast.show("Profile Updated Successfully");
  };

  if (loading) return <SkeletonLoader />;

  return (
    <form onSubmit={handleSave}>
      <Input value={data?.name} label="Full Name" />
      <Button type="submit">Save Changes</Button>
    </form>
  );
};

In the example above, Replay didn't just see a form. It saw the loading state and the success message in the video recording. Because Replay detects application side effects through temporal analysis, it correctly implemented the

and hooks required to make the component functional.

Modernizing Legacy Systems Without the Headache#

Legacy modernization is often stalled by the fear of breaking "invisible" logic. When you move from a monolithic jQuery application to a modern React architecture, the "invisible" logic is usually hidden in global event listeners or side-effect-heavy functions.

Because Replay detects application side behaviors visually, it bypasses the need to understand the messy legacy source code. If the legacy app behaves a certain way on screen, Replay captures that behavior and ports it to the new Design System.

This is particularly useful for Legacy Modernization projects where the backend is staying the same, but the frontend is being completely reimagined. You can sync your new Figma design tokens using the Replay Figma Plugin and then map the legacy behaviors onto the new components.

Powering AI Agents with the Headless API#

The future of development isn't just humans using tools; it's AI agents like Devin or OpenHands performing migrations autonomously. Replay provides a Headless API (REST + Webhooks) that allows these agents to "see" the application.

When an AI agent uses Replay, it doesn't just get a DOM tree. It gets a temporal map of the application. If the agent is tasked with "Fix the checkout bug," it can watch a Replay recording of the bug occurring. Replay detects application side failures in the video—like a button that clicks but doesn't trigger a redirect—and provides the agent with the exact code block that needs fixing.

This "Agentic Editing" is far more precise than traditional search-and-replace. It allows for surgical updates to complex components without regressing other features.

Automating E2E Tests from Recordings#

One of the most tedious parts of modernization is writing tests to ensure parity between the old and new systems. Replay automates this by generating Playwright or Cypress tests directly from your screen recordings.

As the video plays, Replay identifies the selectors and the assertions. If a success message appears at the 10-second mark in the video, Replay writes a test assertion to check for that message in the new code. This ensures that the modernized version of the app maintains the exact behavioral specs of the original.

javascript
// Playwright test generated by Replay temporal analysis
import { test, expect } from '@playwright/test';

test('User can update profile successfully', async ({ page }) => {
  await page.goto('/profile/edit');
  
  // Replay detected interaction with the input field
  await page.fill('input[name="name"]', 'Jane Doe');
  
  // Replay detected the click event and the subsequent side effect
  await page.click('button[type="submit"]');
  
  // Replay detected the visual appearance of the success toast
  const toast = page.locator('.toast-success');
  await expect(toast).toBeVisible();
  await expect(toast).toHaveText('Profile Updated Successfully');
});

Scaling with Replay’s Component Library#

As you record more of your application, Replay builds a centralized Component Library. This isn't just a folder of files; it's a living design system extracted from your production environment.

Each component in the library is associated with the video context it was extracted from. If a developer needs to know how the

component handles sorting, they can watch the original video directly from the documentation. This level of traceability is why Replay is the preferred choice for regulated environments (SOC2, HIPAA) where audit trails are mandatory.

For more on how to manage these assets, check out our guide on Auto-Generating Design Systems.

Why Video-to-Code is the New Standard#

The industry is moving away from static handoffs. The friction between design, product, and engineering usually occurs in the "gaps" between static states. By using video as the primary unit of context, Replay eliminates those gaps.

Video-to-code is the process of converting a screen recording into production-ready React components, complete with styling, state management, and side-effect logic. Replay is the first platform to leverage temporal computer vision to achieve this.

When Replay detects application side transitions, it is performing a task that would take a human hours of painstaking work. It is turning "visual pixels" into "logical intent." This is the core of Visual Reverse Engineering.

Frequently Asked Questions#

How does Replay detect application side effects without access to the original source code?#

Replay uses temporal video analysis and computer vision. By observing how the UI responds to specific user inputs over time (e.g., a button click followed by a loading spinner and then a data update), Replay infers the underlying state transitions and side effects like API calls.

Can Replay handle complex state management like Redux or XState?#

Yes. While Replay doesn't "read" the Redux store directly from a video, it observes the visual manifestations of state changes. It then generates clean, modern React state logic (using hooks or context) that mirrors the observed behavior, making it easier to integrate into any modern state management architecture.

Is Replay suitable for highly secure or on-premise environments?#

Replay is built for regulated industries. We offer SOC2 compliance, HIPAA-ready configurations, and On-Premise deployment options for enterprises that cannot use cloud-based AI tools for their proprietary codebases.

How much faster is Replay compared to manual frontend rewrites?#

According to our internal benchmarks and user data, Replay reduces the manual effort of UI reconstruction by 90%. A task that typically takes 40 hours of manual coding and design matching can be completed in approximately 4 hours using the Replay Method.

Does Replay support frameworks other than React?#

Currently, Replay is optimized for generating pixel-perfect React components and design systems. However, the Headless API and the underlying temporal analysis data can be used by AI agents to generate code in other frameworks like Vue, Svelte, or Angular.

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