Natural/ADABAS Logic Extraction: A Complete Guide for Public Sector CTOs

The mainframe is not just a technology problem; it is a high-stakes dependency trap. For public sector CTOs, the "black box" of Natural/ADABAS environments represents a critical failure point where the original developers have long since retired, and the documentation is non-existent. When a legacy system manages state-wide unemployment benefits or federal tax records, "guessing" the business logic is not an option.

Traditional modernization efforts fail because they attempt to translate code line-by-line. This approach ignores the reality that the UI behavior and the underlying data relationships are what actually define the service. To move forward, agencies need a naturaladabas logic extraction complete strategy that bridges the gap between green screens and modern React-based architectures without the 18-month lead time.

TL;DR: Manual extraction of Natural/ADABAS logic takes an average of 40 hours per screen and has a 70% failure rate in the public sector. Replay uses Visual Reverse Engineering to convert recorded user workflows into documented React components and Design Systems, reducing modernization timelines from years to weeks. This guide outlines how to achieve a naturaladabas logic extraction complete transition by focusing on visual behavior rather than just back-end code.

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The High Cost of Inaction: Why Manual Extraction Fails#

The global technical debt has ballooned to $3.6 trillion, and a significant portion of that is locked in Software AG’s Natural/ADABAS ecosystems. For government agencies, the risk is compounded by the "brain drain." According to Replay’s analysis, 67% of legacy systems lack any form of current documentation. When the person who wrote the original Natural logic in 1984 is no longer available, the code becomes a "load-bearing" mystery.

Industry experts recommend moving away from manual "code-scraping." Manual extraction is prone to human error, where a developer might misunderstand a

or statement in Natural, leading to data corruption in the new system.

Visual Reverse Engineering is the process of capturing legacy UI interactions (like green-screen terminals) and automatically generating modern code, documentation, and logic flows based on those real-world recordings.

The Statistics of Failure#

By utilizing Replay, agencies can bypass the "analysis paralysis" phase. Instead of hiring expensive consultants to read millions of lines of Natural code, you record the workflows as they happen.

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Achieving a NaturalADABAS Logic Extraction Complete Strategy#

To achieve a naturaladabas logic extraction complete state, you must look at the system through three lenses: the User Workflow (Flows), the UI Components (Library), and the Business Rules (Logic).

1. Capturing the "Source of Truth"#

In an ADABAS environment, the "Source of Truth" isn't just the code—it’s how the data is handled during a live transaction. Replay allows users to record these transactions. The platform’s AI Automation Suite then analyzes the video to identify patterns, field validations, and navigation paths.

2. Mapping ADABAS Files to Modern Schemas#

ADABAS is a non-relational, inverted list database. Mapping this to a modern PostgreSQL or NoSQL environment is the hardest part of a naturaladabas logic extraction complete project.

According to Replay's analysis, the most successful migrations use a "Component-First" approach. By defining the UI components first, you can work backward to see exactly what data fields are required, rather than trying to migrate thousands of unused legacy tables.

3. Logic Decoupling#

Natural logic often mixes UI concerns with business rules. For example, a

statement handles both validation and screen control. A modern architecture requires these to be separated.

Modernizing Legacy Flows involves identifying these merged points and decoupling them into clean, functional React hooks.

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Why Manual Methods Fail the NaturalADABAS Logic Extraction Complete Requirement#

Manual extraction relies on "archeology." Developers look at code like the snippet below and try to guess the intent:

natural
/* Legacy Natural Code Snippet */
FIND EMPLOYEES WITH NAME = 'SMITH'
  IF LEAVE-DUE > 20
    MOVE 'EXCESSIVE' TO STATUS-MSG
    PERFORM CALCULATE-PAYROLL
  END-IF
  DISPLAY NAME STATUS-MSG
END-FIND

In a manual rewrite, a developer might miss the implicit global variables or the specific ADABAS file constraints. This is why 70% of legacy rewrites fail or exceed their timeline. Replay changes the paradigm by focusing on the output. If the user sees "EXCESSIVE" on the screen when "LEAVE-DUE" is 21, Replay captures that logic visually and documents it in the Blueprints (Editor).

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Implementing the Modern Stack: From Natural to React#

Once you have initiated your naturaladabas logic extraction complete workflow through Replay, the platform generates clean, documented TypeScript and React code. This isn't "spaghetti code" generated by an old-school transpiler; it’s modular, accessible, and follows modern Design System principles.

Example: The Modernized Component#

Here is how a legacy "Employee Search" screen from a Natural/ADABAS environment is transformed into a modern React component using Replay’s output:

typescript
import React, { useState } from 'react';
import { Button, Input, Table, Badge } from '@/components/ui';

// Logic extracted via Replay Visual Reverse Engineering
interface Employee {
  id: string;
  name: string;
  leaveDue: number;
  status: 'NORMAL' | 'EXCESSIVE';
}

export const EmployeeSearch: React.FC = () => {
  const [employees, setEmployees] = useState<Employee[]>([]);
  
  const handleSearch = async (name: string) => {
    // Replay identifies the API endpoint needed based on legacy flow
    const data = await fetch(`/api/v1/employees?name=${name}`);
    const results = await data.json();
    setEmployees(results);
  };

  return (
    <div className="p-6 bg-white rounded-lg shadow-md">
      <h2 className="text-xl font-bold mb-4">Employee Management</h2>
      <Input 
        placeholder="Search by name (e.g., SMITH)" 
        onChange={(e) => handleSearch(e.target.value)}
      />
      <Table className="mt-4">
        <thead>
          <tr>
            <th>Name</th>
            <th>Leave Due</th>
            <th>Status</th>
          </tr>
        </thead>
        <tbody>
          {employees.map(emp => (
            <tr key={emp.id}>
              <td>{emp.name}</td>
              <td>{emp.leaveDue}</td>
              <td>
                <Badge variant={emp.status === 'EXCESSIVE' ? 'destructive' : 'default'}>
                  {emp.status}
                </Badge>
              </td>
            </tr>
          ))}
        </tbody>
      </Table>
    </div>
  );
};

The Role of the AI Automation Suite#

Replay’s AI doesn't just copy the UI; it understands the intent. For a naturaladabas logic extraction complete project, the AI Automation Suite looks for:

• •Field Constraints: Identifying that a "Social Security" field must be 9 digits.
• •Conditional Logic: Recognizing that the "Submit" button only enables when specific fields are filled.
• •Error Handling: Capturing the specific error messages triggered by the ADABAS back-end.

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Strategic Advantages for Regulated Environments#

Public sector agencies—including Financial Services, Healthcare, and Government—operate under strict compliance mandates. Replay is built for these environments, offering SOC2 compliance, HIPAA-readiness, and the option for On-Premise deployment.

When performing a naturaladabas logic extraction complete migration, the audit trail is paramount. Replay provides a "Visual Audit," where every line of new React code can be traced back to a specific timestamp in a recording of the legacy system. This eliminates the "trust me" factor of manual rewrites.

Building Design Systems for Government is a critical next step. Once Replay extracts the components, they are housed in the Library (Design System), ensuring that every new application built by the agency shares the same look, feel, and accessibility standards (WCAG 2.1).

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Managing the Transition: The 4-Step Replay Framework#

To ensure your naturaladabas logic extraction complete initiative succeeds, follow this framework:

Step 1: Record and Map#

Subject Matter Experts (SMEs) record their daily workflows using Replay. This captures the "hidden" logic that isn't in the documentation.

Step 2: Analyze and Extract#

Replay’s AI processes the recordings to generate Flows (Architecture). This visual map shows how different screens and Natural subprograms interact.

Step 3: Refine in Blueprints#

Developers use the Blueprints (Editor) to refine the generated React code. This is where you can swap out legacy API calls for modern REST or GraphQL endpoints.

Step 4: Deploy and Scale#

The documented components are exported to your CI/CD pipeline. Because Replay saves 70% of the time usually spent on manual coding, you can move from a single pilot to a full-scale agency modernization in months.

typescript
// Example of a decoupled Business Logic Hook extracted from ADABAS logic
export const usePayrollValidation = () => {
  const validateLeave = (leaveDue: number): string => {
    // This logic was extracted from the legacy Natural 'CALCULATE-PAYROLL' routine
    if (leaveDue > 20) {
      return 'EXCESSIVE';
    }
    return 'NORMAL';
  };

  return { validateLeave };
};

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The Future of Legacy Modernization#

The 18-month average enterprise rewrite timeline is no longer acceptable. In a world of rapid policy changes and citizen expectations, the public sector must move faster. A naturaladabas logic extraction complete approach using Replay allows CTOs to de-risk their modernization roadmap.

By focusing on Visual Reverse Engineering, you are not just migrating code; you are preserving the institutional knowledge of your agency while shedding the technical debt of the 20th century.

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Frequently Asked Questions#

What makes a naturaladabas logic extraction complete strategy different from screen scraping?#

Screen scraping merely "skins" an old system, keeping the legacy logic and technical debt intact. A naturaladabas logic extraction complete strategy using Replay actually extracts the underlying business rules and UI patterns to generate entirely new, standalone React code that can run on modern cloud infrastructure.

How does Replay handle complex ADABAS data relationships?#

Replay captures the data flow between the UI and the database. By recording various user scenarios (e.g., "Customer with multiple addresses" vs. "Customer with no address"), Replay’s AI identifies the underlying ADABAS file structures and maps them to modern JSON objects or relational schemas.

Can Replay be used in secure, air-gapped government environments?#

Yes. Replay is designed for regulated industries and offers an On-Premise deployment model. This ensures that sensitive data never leaves your secure environment during the naturaladabas logic extraction complete process.

Does this replace my existing developers?#

No. Replay is a "force multiplier." It automates the tedious, manual parts of the modernization—like documenting 40-year-old code—so your developers can focus on building new features and improving the user experience. It reduces the manual effort from 40 hours per screen to just 4 hours.

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Ready to modernize without rewriting from scratch? Book a pilot with Replay