Predictive Modernization: Using Video Data to Map Complex User Journeys

The average enterprise rewrite takes 18 months and has a 70% chance of failure before the first line of production code is even shipped. For the modern CTO, legacy modernization isn't a technical challenge—it’s a visibility crisis. When 67% of legacy systems lack up-to-date documentation, every migration project begins with "software archaeology," a manual, error-prone process of digging through undocumented COBOL, Java, or Delphi codebases to understand what the system actually does.

Predictive modernization changes the paradigm. Instead of reading dead code, we record live intent. By using video data as the primary source of truth, architects can map complex user journeys and extract functional requirements with surgical precision.

TL;DR: Predictive Modernization: Using video-based reverse engineering allows enterprises to bypass the "archaeology" phase of legacy migration, reducing screen extraction time from 40 hours to 4 hours and cutting overall timelines by 70%.

The $3.6 Trillion Blind Spot#

Global technical debt has reached a staggering $3.6 trillion. Most of this debt is locked inside "black box" systems—applications that run the business but are understood by no one currently on the payroll. The traditional approach to solving this is the "Big Bang" rewrite: a high-risk, 24-month marathon that usually ends in a budget overrun or a cancelled project.

The problem is the discovery phase. Manually auditing a single complex enterprise screen—including its hidden validation logic, API dependencies, and state transitions—takes an average of 40 hours. When you multiply that by 500+ screens in a typical Financial Services or Healthcare ERP, the math simply doesn't work.

Comparison of Modernization Strategies#

What is Predictive Modernization?#

Predictive modernization is the process of using AI-assisted visual reverse engineering to map the "as-is" state of an application through user interaction rather than source code analysis. By recording a real user workflow, Replay captures every DOM change, network request, and state transition.

This data is then used to "predict" the necessary architecture for the modern equivalent. It’s the difference between trying to rebuild a car by looking at a pile of rusted parts (the code) versus watching the car drive a lap and capturing its performance telemetry (the video).

💡 Pro Tip: Don't start with the code. Start with the workflow. The code often contains "ghost logic"—features that are no longer used but still exist in the repo. Video data ensures you only modernize what actually provides value to the business.

The 4-Step Framework for Video-Driven Extraction#

Using Replay, the transition from a legacy black box to a documented React codebase follows a structured, repeatable path.

Step 1: Recording the Source of Truth#

Instead of interviewing subject matter experts (SMEs) for weeks, have them perform their standard daily tasks while Replay records the session. This isn't just a screen recording; it's a deep capture of the application's behavior.

• •Capture edge cases that aren't in the manual.
• •Identify hidden validation rules.
• •Map the actual sequence of API calls.

Step 2: Flow Mapping and Architecture Discovery#

Replay’s "Flows" feature takes the raw video data and converts it into a visual architectural map. This identifies the "gravity" of your application—the areas where the most complex logic resides.

💰 ROI Insight: Companies using Replay see a 70% average time savings in the discovery phase. What used to take months of stakeholder interviews is now accomplished in a single afternoon of recording.

Step 3: Automated Component Extraction#

Once the journey is mapped, Replay’s AI Automation Suite extracts the visual elements and business logic into modern React components. This isn't a "transpiler" that creates unreadable code; it's a clean-room reconstruction based on observed behavior.

typescript
// Example: Generated React component from a Replay video extraction
// Legacy System: Insurance Claims Portal (v4.2)
// Target: Modern React + Tailwind + Headless UI

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

export const ClaimsValidationForm: React.FC<{ claimId: string }> = ({ claimId }) => {
  const { data, loading, error } = useClaimsData(claimId);
  const [status, setStatus] = useState<'pending' | 'approved' | 'denied'>(data?.status);

  // Business logic preserved from legacy video extraction:
  // Rule: If claim amount > $5000, secondary approval is required.
  const requiresSecondaryApproval = data?.amount > 5000;

  const handleSubmission = async (e: React.FormEvent) => {
    e.preventDefault();
    // API Contract generated by Replay analysis
    await fetch(`/api/v1/claims/${claimId}/validate`, {
      method: 'POST',
      body: JSON.stringify({ status, requiresSecondaryApproval }),
    });
  };

  if (loading) return <SkeletonLoader />;

  return (
    <form onSubmit={handleSubmission} className="p-6 bg-white rounded-lg shadow-md">
      <h2 className="text-xl font-bold mb-4">Claim Review: {claimId}</h2>
      {/* Dynamic fields extracted from legacy UI structure */}
      <div className="space-y-4">
        <label className="block text-sm font-medium text-gray-700">Adjuster Notes</label>
        <textarea 
          defaultValue={data?.notes}
          className="mt-1 block w-full border-gray-300 rounded-md shadow-sm"
        />
      </div>
      <button type="submit" className="mt-6 w-full bg-blue-600 text-white py-2 rounded-md">
        Submit Decision
      </button>
    </form>
  );
};

Step 4: API Contract and E2E Test Generation#

The final step in predictive modernization is ensuring the new system talks to the old backend (or its replacement) correctly. Replay analyzes the network traffic captured during the video session to generate OpenAPI (Swagger) specifications and Playwright/Cypress E2E tests.

yaml
# Generated API Contract from Replay Network Trace
openapi: 3.0.0
info:
  title: Legacy Claims API
  version: 1.0.0
paths:
  /api/v1/claims/{claimId}/validate:
    post:
      summary: Extracted from user workflow "Standard Claim Approval"
      parameters:
        - name: claimId
          in: path
          required: true
          schema:
            type: string
      requestBody:
        content:
          application/json:
            schema:
              type: object
              properties:
                status:
                  type: string
                  enum: [pending, approved, denied]
                requiresSecondaryApproval:
                  type: boolean

Solving the "Archaeology" Problem in Regulated Industries#

For Financial Services, Healthcare, and Government agencies, modernization isn't just about speed—it's about compliance. You cannot "move fast and break things" when dealing with HIPAA-protected data or SOC2 requirements.

Manual documentation gaps are a massive liability during audits. Replay provides a "Technical Debt Audit" that acts as a paper trail for the modernization process. By using video as the source of truth, you have an immutable record of why a specific piece of logic was built the way it was.

⚠️ Warning: Most AI-driven code assistants hallucinate logic when they don't understand the context. Replay avoids this by anchoring the AI to the actual video frames and network packets—providing a "ground truth" that generic LLMs lack.

The Impact on Technical Decision Makers#

If you are a VP of Engineering or an Enterprise Architect, your primary goal is risk mitigation. Predictive modernization with Replay offers three key advantages:

1. •Elimination of "The Gap": In traditional rewrites, there is a massive gap between what the business thinks the app does and what the code actually does. Replay closes this by documenting the reality of the user experience.
2. •Design System Acceleration: Replay’s "Library" feature automatically groups extracted components into a unified Design System. This ensures that your modernized app isn't just a functional clone, but a consistent, scalable product.
3. •On-Premise Security: Unlike cloud-only AI tools, Replay offers an On-Premise deployment model, ensuring that sensitive legacy data never leaves your secure environment.

Scaling Modernization Across the Enterprise#

Modernizing a single app is a project; modernizing a portfolio is a strategy. To scale this approach, organizations should establish a "Modernization Factory" using Replay.

1. •The Library: Centralize all extracted React components. If three different legacy apps use a similar "Customer Search" grid, Replay identifies the overlap, allowing you to build one modern component that serves all three.
2. •The Blueprints: Use the Replay Editor to refine the extracted code. Architects can set global standards for state management (e.g., Redux vs. Zustand) and styling (e.g., Tailwind vs. CSS Modules) that the AI Suite will follow during extraction.
3. •The Audit: Continuously run technical debt audits on legacy systems to prioritize which screens or workflows should be modernized next based on usage frequency and complexity.

📝 Note: Predictive modernization is not about replacing developers. It’s about replacing the 80% of the work that developers hate—documenting old code, mapping API endpoints, and trying to replicate legacy UI bugs in modern frameworks.

Frequently Asked Questions#

How does Replay handle complex business logic hidden in the backend?#

Replay captures the inputs and outputs of every transaction. While it primarily focuses on the frontend and the API layer, it maps the "behavioral contract" of the backend. This allows developers to see exactly what data the backend expects and what it returns, making it significantly easier to refactor or replace backend services using the generated API contracts.

Can Replay work with "Grey Box" systems where we have some code but no documentation?#

Absolutely. Replay is most powerful when used as a bridge. If you have partial source code, Replay’s AI Suite can cross-reference the video data with the existing codebase to provide an even deeper level of documentation, identifying "dead code" that exists in the repo but never triggers during actual user workflows.

What is the learning curve for an Enterprise Architecture team?#

Most teams are productive within 48 hours. Because Replay is built on visual workflows, the barrier to entry is low. The primary shift is moving from a "code-first" mindset to a "workflow-first" mindset. Once the team sees a legacy screen converted into a clean React component in minutes, the value proposition becomes self-evident.

How does this fit into a CI/CD pipeline?#

Replay generates standard, high-quality TypeScript and React code, along with E2E tests in Playwright or Cypress. This output integrates directly into your existing Git-based workflows. Replay doesn't own your code; it simply accelerates the creation of it.

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Ready to modernize without rewriting? Book a pilot with Replay - see your legacy screen extracted live during the call.