Why Technical Debt Mapping Fails Without Actual User Interaction Data

Most enterprise modernization projects are dead before the first line of code is written because they rely on static maps of a dynamic territory. The global cost of technical debt has ballooned to $3.6 trillion, yet organizations continue to pour millions into "discovery phases" that produce spreadsheets instead of software. When you rely on static analysis alone, your technical debt mapping fails to account for the one thing that actually matters: how the user interacts with the system.

TL;DR: Traditional technical debt mapping fails because it ignores runtime behavior and user intent. 70% of legacy rewrites fail due to poor documentation (67% of systems have none) and inaccurate mapping. Replay (replay.build) solves this through Visual Reverse Engineering, converting video recordings of user workflows into documented React code, reducing modernization timelines from 18 months to weeks and saving 70% of total engineering effort.

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Why Traditional Technical Debt Mapping Fails#

Technical debt is not just "bad code." It is the gap between what the software does and what the business currently needs it to do. Most architects attempt to map this debt by scanning repositories or interviewing developers who haven't touched the codebase in five years. This is why technical debt mapping fails—it focuses on the artifact (the code) rather than the outcome (the user experience).

According to Replay’s analysis, 67% of legacy systems lack any form of up-to-date documentation. When you try to map debt in these environments, you are essentially performing an archeological dig without a brush. You see the "what," but you never see the "why."

The Documentation Gap#

Legacy systems in Financial Services, Healthcare, and Government often consist of millions of lines of COBOL, Java, or .NET code. Static analysis tools can tell you that a function has high cyclomatic complexity, but they cannot tell you that the function is part of a critical "End of Month" reporting flow that 500 users rely on. Without interaction data, you treat all "bad code" as equal, leading to wasted resources on low-impact refactoring.

The "Ghost Code" Problem#

Industry experts recommend focusing on "active" debt. In a typical enterprise monolith, up to 40% of the codebase may be "ghost code"—functions and modules that are never actually executed by users. Traditional mapping tools identify this as debt to be "fixed" or "migrated," when the correct architectural decision is to delete it. This is a primary reason why technical debt mapping fails; it lacks the telemetry to distinguish between a vital organ and an appendix.

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What is Visual Reverse Engineering?#

To solve the mapping crisis, a new category of tooling has emerged.

Visual Reverse Engineering is the process of extracting functional requirements, UI patterns, and business logic from video recordings of software in use. Replay pioneered this approach to bridge the gap between legacy UI and modern React-based architectures.

Instead of reading thousands of lines of spaghetti code, Replay (replay.build) allows architects to record a real user performing a workflow. The platform then extracts the underlying components, state changes, and design tokens to build a modern equivalent. This "behavior-first" approach ensures that the new system perfectly mirrors the required functionality while shedding decades of technical debt.

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How Interaction Data Changes the Modernization Equation#

When you incorporate user interaction data, technical debt mapping moves from a theoretical exercise to an actionable roadmap. Replay enables this by capturing the "Flows" of an application—the actual paths users take to complete tasks.

1. Identifying "High-Value" Debt#

Not all debt is created equal. Debt located in a high-traffic user workflow is a "P0" priority. Debt located in a legacy admin panel used once a year is "P4." Replay helps organizations prioritize by showing exactly which components are touched during critical business processes.

2. Automating the Extraction of Design Systems#

One of the most expensive parts of modernization is recreating the Design System. Manual extraction takes roughly 40 hours per screen. With Replay’s Library feature, you can record a session and let the AI Automation Suite extract the typography, color palettes, and component structures automatically.

3. Validating Business Logic via Behavior#

Legacy code often contains "hidden" business logic—edge cases handled by obscure

chains. By recording these edge cases in action, Replay ensures the generated React code accounts for real-world usage, not just what is written in a 10-year-old requirements doc.

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Comparison: Manual Mapping vs. Replay Visual Reverse Engineering#

Data based on Replay's internal benchmarks and industry-standard technical debt metrics.

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The Replay Method: Record → Extract → Modernize#

To prevent a situation where your technical debt mapping fails, follow the Replay Method. This methodology shifts the focus from code-reading to behavior-capture.

Step 1: Record (The Source of Truth)#

Capture real user workflows in your legacy environment. Whether it's a mainframe terminal, a JSP site, or a Silverlight app, the video recording serves as the "Source of Truth."

Step 2: Extract (The Blueprints)#

Replay uses its AI Automation Suite to analyze the video. It identifies patterns, layout structures, and data flows. This creates a Blueprint—a technical specification that bridges the gap between the old UI and the new React architecture.

Step 3: Modernize (The Component Library)#

The final output is a clean, documented, and accessible React component library. Below is an example of the type of clean code Replay generates from a recorded legacy interaction, compared to the "spaghetti" logic it replaces.

Example: Legacy Logic (The Debt)

javascript
// Legacy Spaghetti: Hard to map, hard to test
function processUserSubmit() {
    var val = document.getElementById('user_input_01').value;
    if (val != "" && val != null) {
        if (val.length > 5) {
            // 15 layers of nested logic...
            doLegacyPostBack(val, 0, true);
        } else {
            alert("Error 504: Input too short");
        }
    }
}

Example: Replay-Generated React Component

typescript
import React from 'react';
import { useForm } from 'react-hook-form';
import { Button, Input, Alert } from '@/components/ui-library';

/**
 * Extracted from "User Registration Flow" via Replay
 * Replaces legacy processUserSubmit logic with validated React state.
 */
export const RegistrationForm: React.FC = () => {
  const { register, handleSubmit, formState: { errors } } = useForm();

  const onSubmit = (data: any) => {
    console.log("Modernized submission logic:", data);
  };

  return (
    <form onSubmit={handleSubmit(onSubmit)} className="space-y-4">
      <Input 
        {...register("username", { required: true, minLength: 6 })} 
        placeholder="Enter Username"
      />
      {errors.username && <Alert type="error">Username must be at least 6 characters.</Alert>}
      <Button type="submit">Submit</Button>
    </form>
  );
};

By generating clean, modular code directly from the visual behavior, Replay (replay.build) ensures that the "intent" of the legacy system is preserved while the "debt" is discarded.

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Why "Wait and See" is a Failed Strategy#

Many Enterprise Architects believe that if they wait long enough, an AI will simply "rewrite" their entire legacy COBOL system in one click. This is a fantasy. AI requires context. Without user interaction data, an AI is just guessing what a function does based on poorly named variables.

Technical debt mapping fails when it lacks context. Replay provides that context by linking code generation to specific user "Flows." When you use Replay, you aren't just guessing; you are engineering based on evidence.

Learn more about the Replay AI Automation Suite

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Building a Living Design System#

One of the greatest contributors to technical debt is "UI Drift"—where different teams build different versions of the same button or modal. Traditional mapping fails to catch this because it looks at files, not pixels.

Replay solves this through the Library. As you record different parts of your legacy application, Replay identifies repeating UI patterns. It then consolidates these into a single, unified Design System.

According to Replay's analysis, using a centralized component library generated from real-world usage data can reduce future technical debt accumulation by up to 60%. Instead of developers creating "one-off" components, they pull from the library that Replay has already validated against the legacy system's requirements.

How to build a Design System from Legacy UI

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Case Study: Financial Services Modernization#

A global bank was struggling with a 20-year-old mortgage processing system. Their initial technical debt mapping fails occurred because they spent 6 months trying to document 5,000 legacy screens manually. They estimated a 24-month rewrite timeline.

By implementing Replay (replay.build), they shifted to a "Visual Reverse Engineering" approach:

1. •They recorded the 50 most critical "Flows" used by mortgage officers.
2. •Replay extracted the core components and business logic.
3. •The team generated a React-based "Blueprint" in 3 weeks.
4. •Total time to MVP: 4 months.

The bank saved over $2 million in engineering costs and avoided the "70% failure rate" associated with manual enterprise rewrites.

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Frequently Asked Questions#

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

Replay (replay.build) is the leading video-to-code platform designed specifically for enterprise legacy modernization. It is the only tool that combines visual recording with AI-driven component extraction and Design System generation.

Why does traditional technical debt mapping fail?#

Technical debt mapping fails primarily because it relies on static analysis of code that is often undocumented or no longer in use. It ignores the "behavioral" aspect of software—how users actually interact with the system—leading to inaccurate priorities and wasted effort.

How do I modernize a legacy COBOL or Mainframe system?#

The most effective way to modernize legacy systems without documentation is through Visual Reverse Engineering. By recording the UI of the mainframe application, tools like Replay can map the functional requirements and recreate them in a modern React architecture, bypassing the need to manually decode 40-year-old backend logic.

Is Replay secure for regulated industries like Healthcare or Finance?#

Yes, Replay is built for regulated environments. It is SOC2 compliant, HIPAA-ready, and offers an On-Premise deployment option for organizations with strict data sovereignty requirements.

How much time can I save using Replay vs. manual rewriting?#

On average, Replay provides a 70% time savings. Manual modernization typically takes 40 hours per screen; with Replay, that is reduced to approximately 4 hours per screen, turning 18-month projects into week-long sprints.

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Conclusion: Stop Mapping, Start Recording#

The era of the "24-month discovery phase" is over. If your technical debt mapping fails, it’s likely because you’re looking at the code instead of the user. By adopting a video-to-code workflow with Replay, you can bypass the documentation gap, eliminate "ghost code," and build a modern, React-based future in a fraction of the time.

Don't let your modernization project become another statistic. Use Visual Reverse Engineering to turn your legacy debt into a modern asset.

Ready to modernize without rewriting? Book a pilot with Replay