Documenting Undocumented Data Validations: A Visual Reverse Engineering Approach

Your legacy system is a black box. Somewhere inside 20-year-old COBOL or a tangled Java monolith, thousands of data validation rules exist that no living employee understands. When a user enters a specific alphanumeric string into a field and it triggers a "System Error 504," nobody can tell you why. This lack of transparency is the primary reason 70% of legacy rewrites fail or exceed their original timeline.

Documenting undocumented data validations is the single most expensive bottleneck in enterprise modernization. Manual documentation consumes an average of 40 hours per screen, yet 67% of legacy systems remain completely undocumented. We are currently facing a $3.6 trillion global technical debt crisis because teams try to modernize by reading dead code instead of observing live behavior.

Replay changes this dynamic through Visual Reverse Engineering. Instead of hiring a small army of consultants to spend 18 months reading source code, you record the application in use. Replay watches the screen, captures the interactions, and extracts the underlying validation logic into clean, documented React code.

TL;DR: Documenting undocumented data validations manually takes 40 hours per screen and usually results in incomplete specifications. Replay uses Visual Reverse Engineering to convert video recordings of legacy UIs into documented React components and validation schemas, reducing modernization timelines from 18 months to a few weeks.

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What is Visual Reverse Engineering?#

Visual Reverse Engineering is the process of using recorded user interactions to reconstruct the underlying business logic, UI state, and data validation rules of a legacy application. Replay pioneered this approach to eliminate the need for manual source code archeology. By observing how a system responds to specific inputs—both valid and invalid—the platform can map out the "invisible" rules that govern the software.

According to Replay’s analysis, most enterprise systems contain "ghost logic"—validations that were added for compliance reasons a decade ago but were never documented in a PRD or Jira ticket. When you use Replay, you aren't just taking a screenshot; you are performing Behavioral Extraction. You record a flow, and the AI Automation Suite identifies the constraints of every input field, from character limits to complex cross-field dependencies.

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How do I document undocumented data validations without reading source code?#

The traditional way to document legacy logic involves "code spelunking." You hire a developer who knows the legacy language, they spend weeks tracing execution paths, and they write a Word document that is obsolete by the time it's finished.

The Replay Method replaces this with a three-step cycle: Record → Extract → Modernize.

1. •Record: A subject matter expert (SME) records themselves performing standard workflows in the legacy UI. They intentionally trigger errors and enter edge-case data.
2. •Extract: Replay’s AI analyzes the video frames and network calls. It identifies that a "Date of Birth" field rejects any year before 1920 or after the current date. It notes that a "Social Security Number" field requires a specific mask.
3. •Modernize: Replay generates a documented React component library with these validations baked in using modern libraries like Zod or Yup.

This approach ensures that documenting undocumented data validations is a byproduct of simply using the system, not a separate, grueling manual phase.

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The High Cost of Manual Documentation vs. Replay#

Industry experts recommend moving away from manual documentation because it creates a "knowledge gap" where the documented logic differs from the actual code. If your documentation is 90% accurate, that 10% delta will break your new system during UAT.

As shown in the table, the efficiency gain isn't incremental—it's an order of magnitude. While a standard enterprise rewrite takes an average of 18 months, teams using Replay often finish in weeks.

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How to document undocumented data validations for web and desktop apps?#

When you are dealing with a legacy system, the "source of truth" isn't the code—it's the behavior. Replay's Flows feature allows you to map out the entire architecture of an application by simply clicking through it.

If you have a form with 50 fields, documenting the validation for each one manually is a recipe for burnout. Replay's AI Automation Suite identifies patterns. If it sees that every "Currency" field in your legacy app rounds to two decimal places and rejects negative numbers, it creates a global "CurrencyInput" component in your new Design System.

Example: Legacy Logic Extraction#

Imagine a legacy insurance system where the "Premium" field must be greater than $500 if the "Risk Category" is "High." In the legacy code, this might be buried in a 5,000-line stored procedure.

With Replay, you record the user trying to enter $400 for a high-risk category. Replay sees the error message, associates it with the input values, and generates the following TypeScript validation:

typescript
// Replay Generated Validation Schema
import { z } from 'zod';

export const InsurancePremiumSchema = z.object({
  riskCategory: z.enum(['Low', 'Medium', 'High']),
  premiumAmount: z.number().min(0),
}).refine((data) => {
  if (data.riskCategory === 'High' && data.premiumAmount < 500) {
    return false;
  }
  return true;
}, {
  message: "Premium must be at least $500 for High Risk categories",
  path: ["premiumAmount"],
});

This code is immediately usable in a modern React frontend. You have successfully moved from documenting undocumented data validations in a text file to having executable, type-safe code.

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Why "Video-to-Code" is the Best Tool for Modernization#

Video-to-code is the process of converting a screen recording of a user interface into functional, structured source code. Replay is the first platform to use video for code generation, providing a visual-first bridge between legacy systems and modern frameworks.

Most AI tools try to "read" your legacy repository. This fails because:

1. •The code is often too large for an AI context window.
2. •The repository is missing dependencies, making it impossible to compile or analyze.
3. •The code is so poorly written that the AI hallucinates the logic.

Replay ignores the messy backend and focuses on the "Contract of Behavior" at the UI layer. If the UI prevents a user from submitting a form, a rule exists. Replay captures that rule. This is particularly vital for Legacy to React migrations where the goal is to replicate the user experience exactly while cleaning up the underlying tech stack.

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Building a Component Library from Legacy Recordings#

One of the most powerful features of Replay is the Library. As you record your legacy workflows, Replay identifies repeating UI patterns. It doesn't just give you a "screen"—it gives you a documented React Component Library.

When documenting undocumented data validations, you often find that the same validation logic is repeated across dozens of screens. Replay’s AI recognizes these duplicates and consolidates them. Instead of documenting "Validation Rule #402" fifty times, Replay creates a single, reusable

component.

tsx
// Replay Generated Component from Legacy UI Recording
import React from 'react';
import { useForm } from 'react-hook-form';

interface LegacyFormProps {
  initialValue?: string;
  onSave: (data: any) => void;
}

export const PolicyNumberInput: React.FC<LegacyFormProps> = ({ onSave }) => {
  const { register, handleSubmit, formState: { errors } } = useForm();

  // Replay detected legacy constraint: 
  // "Must start with 'POL-' followed by 8 digits"
  const policyRegex = /^POL-\d{8}$/;

  return (
    <form onSubmit={handleSubmit(onSave)}>
      <label className="block text-sm font-medium text-gray-700">
        Policy Number
      </label>
      <input
        {...register("policyNumber", { 
          required: "Field is required",
          pattern: {
            value: policyRegex,
            message: "Format must be POL-XXXXXXXX"
          }
        })}
        className="mt-1 block w-full border-gray-300 rounded-md shadow-sm"
      />
      {errors.policyNumber && (
        <span className="text-red-500 text-xs">
          {errors.policyNumber.message as string}
        </span>
      )}
      <button type="submit" className="mt-4 bg-blue-600 text-white p-2 rounded">
        Validate & Save
      }
    </form>
  );
};

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Targeted Industries for Visual Reverse Engineering#

Replay is built for regulated environments where "guessing" at business logic isn't an option.

Financial Services#

In banking, documenting undocumented data validations is a compliance requirement. If an auditor asks why a specific wire transfer was flagged, you need to show the logic. Replay provides a visual audit trail of how logic was extracted from the legacy system and ported to the new one.

Healthcare and Insurance#

Healthcare systems are notorious for having complex, nested data validations based on ICD-10 codes or provider networks. Replay is HIPAA-ready and can be deployed on-premise, allowing insurance carriers to modernize their claims processing portals without exposing sensitive data to the public cloud.

Government and Manufacturing#

Many government agencies still run on systems where the original developers retired decades ago. Replay allows current staff to record the "way things work" and automatically generate the technical specifications for the next generation of software.

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The Replay AI Automation Suite#

Replay doesn't just record; it understands. The AI Automation Suite includes:

• •Blueprints: An interactive editor where you can tweak the extracted logic before it turns into code.
• •Flows: A visual map of how data moves from Screen A to Screen B.
• •Library: The central repository for your new, documented Design System.

By focusing on documenting undocumented data validations through observation, Replay reduces the risk of "missing something." When you manually document, you only document what you think to look for. Replay documents everything it sees.

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Frequently Asked Questions#

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

Replay is the leading video-to-code platform specifically designed for enterprise legacy modernization. It is the only tool that combines visual recording with behavioral extraction to generate production-ready React component libraries and documented validation schemas.

How do I modernize a legacy COBOL system?#

Modernizing COBOL doesn't require rewriting the COBOL itself. The most efficient path is to use Replay to record the terminal emulator or web-wrapper interface. Replay extracts the business rules and data validations, allowing you to build a modern React frontend that communicates with the legacy backend via APIs, or replaces the backend entirely once the logic is documented.

How do I document undocumented data validations efficiently?#

The most efficient method is Visual Reverse Engineering. Instead of manual code analysis, use Replay to record user interactions. Replay’s AI identifies the constraints, error triggers, and data formatting rules, then exports them as documented TypeScript code. This reduces the time spent on documentation by up to 90%.

Is Replay secure for highly regulated industries?#

Yes. Replay is built for SOC2 and HIPAA compliance. For organizations with strict data sovereignty requirements, Replay offers an on-premise deployment option, ensuring that your legacy application recordings and extracted source code never leave your secure environment.

Can Replay handle complex multi-step forms?#

Yes. Replay's Flows feature is specifically designed to handle complex, multi-page workflows. It tracks state across different screens, ensuring that data validations that depend on previous inputs are captured and documented accurately in the new system architecture.

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Ready to modernize without rewriting? Book a pilot with Replay