Solving the Documentation Gap in Legacy Public Sector Pension Systems

The average state pension system relies on business logic written by engineers who retired before the internet was commercialized. In the public sector, "documentation" is often a stack of yellowing binders or, more commonly, a collection of oral traditions passed down between COBOL maintainers. When these systems need to be modernized, the primary obstacle isn't the new technology stack—it’s the "black box" of the old one.

Solving the documentation gap in legacy public sector pension systems is no longer a matter of manual archaeology. It is a matter of technical survival. With a global technical debt mountain reaching $3.6 trillion, the public sector can no longer afford the 18-month "discovery phases" that yield little more than outdated Visio diagrams.

TL;DR: Solving the documentation gap in legacy pension systems requires moving away from manual "code archaeology" toward Visual Reverse Engineering, reducing screen-to-code documentation time from 40 hours to 4 hours.

The Documentation Debt Crisis in Public Sector Pensions#

Legacy pension systems are uniquely difficult to document. They are governed by decades of legislative changes, "grandfathered" clauses, and complex actuarial formulas that exist only in the execution of the code—not in any manual.

Currently, 67% of legacy systems lack any form of usable documentation. For a pension fund managing billions in assets, this isn't just a technical hurdle; it’s a fiduciary risk. When an agency decides to modernize, they typically choose between a "Big Bang" rewrite or a "Strangler Fig" approach. Both are historically prone to disaster in the public sector, where 70% of legacy rewrites fail or significantly exceed their timelines.

The core of the failure is the "Documentation Gap." Architects spend months interviewing users and reading 30-year-old source code to understand what the system does before they can write a single line of new code.

The Cost of Manual Discovery#

💰 ROI Insight: Manual documentation takes an average of 40 hours per screen. With Replay, this is reduced to 4 hours through automated extraction and React component generation.

Why Manual "Archaeology" Fails#

Traditional discovery involves "The Interview Loop." Business analysts sit with pension administrators to record how they process a retirement claim. The administrator performs 50 clicks, navigates three green-screen terminals, and performs a manual calculation in Excel.

The BA writes a 100-page functional requirement document (FRD). By the time the developer reads the FRD, the nuances of the business logic—the "edge cases" that define pension tiers—are lost. This is the documentation gap in action.

The Problem with "Code-First" Discovery#

Reading the source code of a 1980s mainframe system is like reading a dead language. Even if you understand the syntax, you lack the context of the user's intent. You see the how, but never the why.

To bridge this, we need to treat the user workflow as the source of truth. By recording the actual execution of a pension calculation, we can reverse engineer the technical requirements from the visual interactions.

Solving the Documentation Gap with Visual Reverse Engineering#

Visual Reverse Engineering changes the sequence of modernization. Instead of reading code to build a UI, you record the UI to generate the code.

Replay allows architects to record real user workflows in the legacy environment. The platform captures the DOM states, the network calls, and the business logic triggers. It then transforms these recordings into documented React components and API contracts.

Example: Generated Component from Workflow Extraction#

When a pension administrator calculates a Cost of Living Adjustment (COLA), Replay captures the logic and generates a clean, modern equivalent. This isn't just a "screenshot"—it's a functional React component based on the observed data flow.

typescript
// Example: Generated COLA Adjustment Component from Legacy Workflow
// This component preserves the legacy calculation triggers while modernizing the UI
import React, { useState, useEffect } from 'react';
import { Button, Input, Alert } from '@/components/ui';

interface PensionRecord {
  memberId: string;
  currentBenefit: number;
  tier: 'Tier1' | 'Tier2' | 'Tier3';
  lastAdjustmentDate: string;
}

export const COLAAdjustmentForm = ({ recordId }: { recordId: string }) => {
  const [record, setRecord] = useState<PensionRecord | null>(null);
  const [calculatedIncrease, setCalculatedIncrease] = useState<number>(0);

  // Logic extracted from legacy network traffic and UI triggers
  const calculateAdjustment = (base: number, tier: string) => {
    const rates = { Tier1: 0.03, Tier2: 0.02, Tier3: 0.015 };
    return base * (rates[tier as keyof typeof rates] || 0);
  };

  const handleProcess = async () => {
    if (record) {
      const increase = calculateAdjustment(record.currentBenefit, record.tier);
      setCalculatedIncrease(increase);
      // Automatically maps to the generated API contract
      await postAdjustmentUpdate(record.memberId, increase);
    }
  };

  return (
    <div className="p-6 border rounded-lg shadow-sm">
      <h2 className="text-xl font-bold">Annual COLA Adjustment</h2>
      <div className="mt-4 space-y-4">
        <Input label="Member ID" value={record?.memberId} readOnly />
        <Input label="Current Monthly Benefit" value={record?.currentBenefit} readOnly />
        <Alert variant="info">
          Projected Increase: ${calculatedIncrease.toFixed(2)}
        </Alert>
        <Button onClick={handleProcess}>Commit Adjustment</Button>
      </div>
    </div>
  );
};

💡 Pro Tip: Don't try to fix the business logic during the extraction phase. Document the "as-is" state first to ensure parity, then refactor in the new environment.

The 3-Step Process to Modernize Pension Documentation#

Step 1: Workflow Recording and Assessment#

Identify the top 20% of workflows that handle 80% of the pension fund's volume (e.g., New Enrollment, Beneficiary Update, Monthly Payroll Run). Using Replay, capture these workflows as "sessions." This replaces months of manual interviewing with minutes of recording.

Step 2: Automated Extraction and Blueprinting#

Once the workflows are recorded, the platform's AI Automation Suite analyzes the interactions. It generates:

• •API Contracts: Swagger/OpenAPI specs derived from legacy traffic.
• •E2E Tests: Playwright or Cypress tests that mirror the legacy behavior.
• •Technical Debt Audit: A report identifying which parts of the legacy logic are redundant or circular.

Step 3: Component Generation (The Library)#

The extracted blueprints are converted into a modern Design System. Instead of a "Big Bang" rewrite, you now have a library of documented React components that are 1:1 matches for your legacy screens but built on a modern stack.

json
// Example: Generated API Contract (OpenAPI Snippet)
{
  "path": "/api/v1/pension/calculate-vesting",
  "method": "POST",
  "summary": "Extracted from Legacy Vesting Screen Workflow",
  "parameters": [
    { "name": "yearsOfService", "type": "integer", "required": true },
    { "name": "entryDate", "type": "string", "format": "date", "required": true }
  ],
  "responses": {
    "200": {
      "description": "Vesting percentage and eligibility status",
      "schema": { "$ref": "#/definitions/VestingResult" }
    }
  }
}

Security and Compliance in Regulated Environments#

Public sector pension systems are subject to intense scrutiny, including SOC2, HIPAA (for retiree healthcare components), and strict data residency requirements.

⚠️ Warning: Never use "Black Box" AI tools that require sending sensitive PII (Personally Identifiable Information) to the public cloud.

Replay is built for these environments. It offers On-Premise deployment and air-gapped capabilities. During the recording process, PII can be masked or redacted at the source, ensuring that the generated documentation and code contain only the structural logic, not the sensitive data of citizens.

Key Features for Government Use:#

• •SOC2 & HIPAA-ready: Compliant data handling.
• •On-Premise Available: Keep your code and data within your own infrastructure.
• •Audit Trails: Every extracted component is linked back to the original video source of truth for verification.

From Black Box to Documented Codebase#

The goal of solving the documentation gap isn't just to have a wiki; it's to have a functional, navigable architecture.

By using Replay, enterprise architects can move from a state of "fear-based maintenance" (where no one wants to touch the code for fear of breaking a 40-year-old pension rule) to "informed modernization." You gain a Library of components, a Flow map of your architecture, and Blueprints for your future state.

• •Eliminate Archaeology: Stop digging through 1980s code.
• •70% Time Savings: Move from 18-month discovery phases to 2-week extraction sprints.
• •Verify with Video: Use the recording as the ultimate source of truth for business logic.

Frequently Asked Questions#

How long does legacy extraction take?#

While a manual rewrite takes 18-24 months, extraction using Replay typically takes 2 to 8 weeks depending on the number of screens. A single complex pension screen can be documented and converted to a React component in roughly 4 hours.

What about complex business logic hidden in the backend?#

Visual Reverse Engineering captures the inputs, outputs, and state changes. While it documents the "as-is" behavior, Replay also generates the API contracts required to interface with or replace that backend logic. This ensures that even if the backend remains a mainframe for a period, the documentation gap is closed.

Does this replace my developers?#

No. It empowers them. Instead of spending 80% of their time trying to understand legacy spaghetti code, they spend 100% of their time building the new system using the generated blueprints and components as a foundation.

How does this handle "grandfathered" pension rules?#

By recording specific workflows for different "tiers" of members, Replay captures the distinct logic paths for each. The resulting documentation explicitly defines the differences in data handling between, for example, a "Tier 1" employee hired in 1978 and a "Tier 3" employee hired in 2023.

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