The "Black Box" Problem: Using Synthetic User Testing from Video to Map Undocumented Legacy Systems

Most enterprise modernization projects don’t fail because of poor coding; they fail because of a lack of knowledge. When 67% of legacy systems lack any form of up-to-date documentation, developers aren't just refactoring—they are archeologists digging through layers of "black box" logic. Manual discovery is a resource sink, with the average enterprise rewrite timeline stretching to 18 months, often exceeding $3.6 trillion in global technical debt.

The bottleneck is clear: you cannot test or replace what you do not understand. Traditional QA requires a known set of requirements to write test scripts. But in a legacy environment, those requirements are buried in the heads of retired employees or obscured by decades of "spaghetti" code. This is where synthetic user testing from video recordings changes the trajectory of modernization. By recording real user workflows and converting them into documented React components and test suites, platforms like Replay eliminate the discovery phase entirely.

TL;DR: Modernizing undocumented legacy software is a high-risk gamble. Synthetic user testing from video allows teams to bypass manual documentation by recording actual workflows and using AI to generate thousands of QA scenarios and React components. This approach reduces manual effort from 40 hours per screen to just 4 hours, providing a 70% time saving and ensuring that "black box" logic is captured before the old system is decommissioned.

The Documentation Gap and the $3.6 Trillion Debt#

According to Replay's analysis, the average enterprise screen takes 40 hours to manually document, design, and recode. In a system with 500+ screens—common in Financial Services or Insurance—that’s 20,000 man-hours just for the UI layer. When you add the fact that 70% of legacy rewrites fail or exceed their timeline, the financial risk is staggering.

Industry experts recommend moving away from manual "discovery workshops" toward automated capture. The goal is to move from "what we think the system does" to "what the system actually does."

Video-to-code is the process of using computer vision and metadata extraction to convert video recordings of software interfaces into functional code, design tokens, and architectural documentation.

Why Synthetic User Testing from Video is the New Standard#

Traditional synthetic testing relies on predefined scripts. However, if your software is undocumented, your scripts are likely missing edge cases that have existed for 20 years. By utilizing synthetic user testing from video, you are essentially "crowdsourcing" your documentation from the users who know the system best.

Replay captures these workflows through its "Flows" feature, mapping every click, state change, and API call. This data is then used to generate a "Blueprint"—a visual and structural map of the application that serves as the source of truth for the new React-based architecture.

Comparing Modernization Methodologies#

Implementing Synthetic User Testing from Video Recordings#

To implement this at scale, we must move beyond simple screen recording. We need a system that understands the underlying DOM (Document Object Model) or, in the case of legacy "thick clients" (Citrix, Mainframe), the visual intent of the UI.

Step 1: Capturing the "Source of Truth"#

The process begins by recording real users performing their daily tasks. In a regulated environment like Healthcare or Government, these recordings are handled on-premise or within SOC2-compliant pipelines. Replay's engine analyzes the video, identifying recurring UI patterns and state transitions.

Step 2: Generating the Component Library#

Once the video is processed, the AI Automation Suite identifies buttons, inputs, tables, and modals. It doesn't just take a screenshot; it extracts the functional properties.

typescript
// Example: A React component generated via Replay's Visual Reverse Engineering
import React from 'react';
import { LegacyDataTable } from '@replay-internal/ui-library';

interface ClaimsDataProps {
  recordId: string;
  initialState: 'pending' | 'approved' | 'rejected';
}

/**
 * Extracted from Legacy Insurance Portal - Workflow: "Claims Processing"
 * Original System: Silverlight (Legacy)
 * Target System: React/TypeScript
 */
export const ClaimsProcessor: React.FC<ClaimsDataProps> = ({ recordId, initialState }) => {
  const [status, setStatus] = React.useState(initialState);

  // Replay captured the logic: Status change triggers a secondary validation modal
  const handleStatusChange = (newStatus: string) => {
    console.log(`Transitioning record ${recordId} to ${newStatus}`);
    setStatus(newStatus as any);
  };

  return (
    <div className="p-6 bg-slate-50 rounded-lg border border-slate-200">
      <h3 className="text-lg font-bold">Claim ID: {recordId}</h3>
      <LegacyDataTable 
        source="v3_claims_api"
        onAction={handleStatusChange}
        currentStatus={status}
      />
    </div>
  );
};

Modernizing Legacy UI requires more than just a fresh coat of paint; it requires capturing the business logic embedded in the UI transitions.

Step 3: Generating 1,000+ QA Scenarios#

This is where synthetic user testing from video provides the highest ROI. By analyzing 100 hours of video, Replay can identify thousands of permutations in user behavior.

If a user in a Telecom billing system typically clicks "Adjust Balance" and then enters a "Reason Code," the synthetic engine generates tests for:

1. •Valid Reason Code entry.
2. •Missing Reason Code (Error State).
3. •Session timeout during entry.
4. •Rapid double-clicking of the "Submit" button.

Architecture of Synthetic Scenario Generation#

The architecture relies on a "Flow Analysis Engine." This engine maps the visual frames to a state machine.

1. •Visual Ingestion: Replay ingests MP4/WebM streams of legacy workflows.
2. •State Identification: AI identifies "State A" (Login Screen) and "State B" (Dashboard).
3. •Transition Mapping: The "Flow" is defined by the user action (Click, Keypress) that causes the transition.
4. •Synthetic Expansion: The engine varies the inputs (using fuzzing techniques) to create hundreds of "synthetic" versions of that original user flow.

typescript
// Generated Playwright Test Case from Synthetic Video Analysis
import { test, expect } from '@playwright/test';

test.describe('Synthetic Workflow: Claims Approval Path', () => {
  test('should handle rapid status transitions captured in video_ref_882', async ({ page }) => {
    await page.goto('https://modern-app.enterprise.com/claims');
    
    // Select the first claim
    await page.click('[data-testid="claim-row-0"]');
    
    // Replay identified this specific sequence as high-risk for race conditions
    await page.selectOption('[data-testid="status-select"]', 'approved');
    await page.click('[data-testid="submit-btn"]');
    
    // Synthetic variation: Check if the 'Undo' action appears within 2 seconds
    const undoBtn = page.locator('[data-testid="undo-btn"]');
    await expect(undoBtn).toBeVisible({ timeout: 2000 });
    
    // Verify backend sync (Synthetic data injection)
    const statusLabel = page.locator('[data-testid="status-label"]');
    await expect(statusLabel).toHaveText('Processing...');
  });
});

By generating these tests automatically, teams can ensure parity between the legacy system and the modern React application. This is essential because, as Replay's data shows, 18 months is the average enterprise rewrite timeline, and without automated parity testing, bugs introduced in month 3 often don't surface until month 18.

Scaling to 1,000+ Scenarios in Regulated Industries#

In industries like Manufacturing or Telecom, systems are often "air-gapped" or highly sensitive. Replay's ability to run On-Premise allows these organizations to use synthetic user testing from video without their data ever leaving their firewall.

Financial Services: Validating Complex Calculations#

In a legacy banking terminal, interest calculations might be performed client-side in a language like COBOL or Delphi. By recording these calculations and using Replay's AI Automation Suite, architects can verify that the new React microfrontend produces the exact same output for 1,000 different edge-case inputs.

Healthcare: Ensuring HIPAA Compliance#

When recording workflows in a clinical setting, PII (Personally Identifiable Information) must be scrubbed. Replay’s visual processing includes automated PII masking, allowing for the generation of synthetic tests that use "clean" data while maintaining the structural integrity of the clinical workflow.

Design Systems for Enterprise are often the byproduct of this process. As Replay identifies components across different videos, it builds a centralized Library that serves as a unified Design System.

The ROI of Automated Discovery#

The math for a typical Enterprise Architect is simple. If you have a legacy application with 200 screens:

• •Manual Discovery: 200 screens x 40 hours = 8,000 hours. At $150/hr (blended rate), that’s $1.2 million just to document and prep for coding.
• •Replay Discovery: 200 screens x 4 hours = 800 hours. Total cost: $120,000.

This 90% reduction in the discovery phase allows the 70% of projects that would otherwise fail to actually reach production. By using synthetic user testing from video, you aren't just building a new app; you are building a verified, documented, and tested digital asset.

Future-Proofing with Visual Reverse Engineering#

As we look toward the next decade of technical debt, the reliance on human-written documentation will continue to decline. The future belongs to "Self-Documenting Systems." By integrating Replay into the CI/CD pipeline, organizations can continuously record user interactions to update their component libraries and test suites in real-time.

This creates a "living" architecture. When a user finds a new way to interact with the system—perhaps an undocumented shortcut—the system captures it, generates a synthetic test for it, and updates the Blueprint.

Frequently Asked Questions#

What is synthetic user testing from video?#

It is the process of using AI and computer vision to analyze video recordings of real user interactions with software. This analysis is then used to automatically generate functional test scripts, QA scenarios, and even code components that mimic the behavior seen in the video.

How does Replay handle PII and sensitive data in videos?#

Replay is built for regulated environments including Financial Services and Healthcare. It offers PII masking features and can be deployed On-Premise or in a HIPAA-ready cloud environment to ensure that sensitive data is never exposed during the visual reverse engineering process.

Can this work for mainframe or Citrix-based applications?#

Yes. Because Replay uses Visual Reverse Engineering, it does not require access to the underlying source code of the legacy application. It analyzes the visual output (the UI), making it ideal for modernizing "un-hackable" legacy systems like mainframes, SAP GUIs, or Citrix-streamed apps.

How does Replay achieve 70% time savings?#

The majority of modernization time is spent on manual discovery, documentation, and manual QA scripting. Replay automates these three phases by extracting the "Blueprint" of the application directly from user recordings, reducing the time per screen from 40 hours to just 4 hours.

Do I need to be a React expert to use the output?#

No. Replay generates documented React code and TypeScript definitions that follow industry best practices. This provides a massive head start for your development team, allowing them to focus on feature enhancement rather than basic UI reconstruction.

Ready to modernize without rewriting? Book a pilot with Replay