Data Integrity Guarantees: Validating Backend Calls from Reconstructed UIs

Legacy modernization is a game of high-stakes telephone. When you attempt to move business logic from a 20-year-old Delphi, VB6, or PowerBuilder application into a modern React architecture, the greatest risk isn't the UI—it’s the silent corruption of data. If your new frontend sends a

where the legacy COBOL backend expects a fixed-point , or if a validation rule is missed during a manual rewrite, you don't just have a bug; you have a systemic failure of data integrity guarantees validating the very core of your business.

Industry experts recommend that modernization efforts prioritize state synchronization over visual fidelity. Yet, with a global technical debt mountain reaching $3.6 trillion, most enterprises are still stuck in a cycle of manual "rip and replace" that takes 18-24 months and has a 70% failure rate.

TL;DR: Manual modernization of legacy UIs often breaks critical backend contracts. Replay solves this through Visual Reverse Engineering, capturing real-world data flows to generate React components with built-in validation. This reduces the average screen reconstruction time from 40 hours to just 4 hours while ensuring that data integrity guarantees validating backend calls are strictly enforced through automated schema generation.

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The Hidden Cost of Manual Data Mapping#

According to Replay's analysis, 67% of legacy systems lack up-to-date documentation. When developers are tasked with "modernizing" these systems, they are essentially archeologists. They look at a legacy screen, guess the data types based on the UI input fields, and try to reconstruct the API calls.

This manual approach is where data integrity guarantees validating backend inputs often fall apart. A "Date" field in a 1990s insurance platform might look like a standard string but require a specific Julian calendar format in the backend. If the React frontend sends ISO-8601, the database might reject it, or worse, truncate it into a corrupt entry.

Visual Reverse Engineering is the process of recording real user interactions with legacy software to automatically generate structured code, documentation, and architectural flows. By recording the actual traffic between the legacy UI and the backend, Replay eliminates the guesswork that leads to data corruption.

Comparison: Manual Reconstruction vs. Replay Visual Reverse Engineering#

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Implementing Data Integrity Guarantees Validating Legacy Payloads#

To ensure data integrity guarantees validating every interaction, the transition from legacy UI to React must involve a strict schema-first approach. When you use Replay to record a workflow, the platform doesn't just see pixels; it sees the "Flows"—the sequence of events and data packets that define a business process.

Step 1: Capturing the Contract#

In a typical legacy environment (e.g., a Financial Services terminal), the data contract is often implicit. By recording the session, Replay identifies the exact shape of the JSON, XML, or SOAP payload.

Step 2: Generating Type-Safe Wrappers#

Once the flow is captured in the Replay Library, the AI Automation Suite generates TypeScript interfaces that mirror the legacy expectations. This ensures that the React component cannot emit data that violates the backend's constraints.

typescript
// Example of a generated type-safe wrapper from a Replay recording
// Target: Legacy Insurance Policy Update API

export interface PolicyUpdateRequest {
  /** 
   * Legacy ID format: Must be 12 chars, zero-padded
   * Captured from Replay Flow: "PolicyUpdate_01"
   */
  policy_id: string;
  
  /** 
   * Backend expects YYYYMMDD, not ISO string
   */
  effective_date: string; 
  
  coverage_amount: number;
  
  // Replay identified this hidden field required by the legacy mainframe
  checksum_v2: string; 
}

/**
 * Hook to ensure data integrity guarantees validating the payload 
 * before it leaves the client.
 */
export const useUpdatePolicy = () => {
  const validateAndSend = async (data: PolicyUpdateRequest) => {
    if (data.policy_id.length !== 12) {
      throw new Error("Invalid Policy ID format - integrity check failed");
    }
    
    // API Call logic...
    return await fetch('/api/legacy/update', {
      method: 'POST',
      body: JSON.stringify(data),
    });
  };

  return { validateAndSend };
};

Learn more about generating Design Systems from legacy recordings.

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Validating Cross-Field Dependencies#

One of the most complex aspects of data integrity guarantees validating legacy systems is cross-field dependency. In many old systems, the validity of "Field B" depends entirely on the value of "Field A." These rules are rarely documented and are often buried in thousands of lines of stored procedures or COBOL copybooks.

Manual rewrites often miss these dependencies, leading to "Zombie Screens" where the UI looks modern but the application logic is broken. Replay’s "Blueprints" feature allows architects to visualize these dependencies by analyzing the recorded user flows. If a user enters "California" in the state field and the "Tax ID" field suddenly becomes mandatory, Replay captures that state change.

Schema Validation with Zod#

To enforce these data integrity guarantees validating the UI state, we recommend using Zod in conjunction with Replay-generated components.

typescript
import { z } from 'zod';

// Schema generated based on Replay's analysis of a Healthcare Claims workflow
const ClaimSchema = z.object({
  providerId: z.string().min(5),
  claimType: z.enum(['INPATIENT', 'OUTPATIENT']),
  // Cross-field validation: If INPATIENT, hospitalCode is required
  hospitalCode: z.string().optional(),
}).refine((data) => {
  if (data.claimType === 'INPATIENT' && !data.hospitalCode) {
    return false;
  }
  return true;
}, {
  message: "Hospital Code is required for Inpatient claims",
  path: ["hospitalCode"]
});

export type ClaimData = z.infer<typeof ClaimSchema>;

By embedding these rules directly into the reconstructed React components, Replay ensures that the 70% of legacy rewrites that typically fail or exceed timelines are instead delivered with 100% data parity.

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Scaling to Regulated Environments#

For industries like Healthcare (HIPAA), Finance (SOC2), or Government, data integrity guarantees validating backend calls isn't just a technical preference—it's a legal requirement. Replay is built for these high-stakes environments, offering on-premise deployment and SOC2 compliance.

When modernizing a mainframe UI for a bank, for example, you cannot afford a "move fast and break things" mentality. The $3.6 trillion technical debt in these sectors is largely due to the fear of breaking these fragile data contracts.

According to Replay's analysis, using Visual Reverse Engineering allows teams to:

1. •Audit Every Call: Every React component generated is linked back to a recorded "Flow" in the legacy system.
2. •Verify State Transitions: Ensure that the "Architecture" of the new app matches the proven logic of the old one.
3. •Automate Testing: Use the recorded data as the baseline for regression testing the new UI.

Read about modernizing mainframe UIs for financial services.

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The Replay Workflow: From Recording to Validated Code#

The transition from a recording to a production-ready, type-safe React component follows a structured pipeline designed to maximize data integrity guarantees validating the output.

1. •Record: A subject matter expert (SME) records a standard workflow (e.g., "Onboarding a New Client") using the Replay recorder.
2. •Analyze: Replay's AI Automation Suite parses the video and the underlying network/DOM data.
3. •Map: Data fields are mapped to a centralized Library (Design System).
4. •Generate: React code is produced with TypeScript interfaces and validation logic.
5. •Validate: The new component is run against the original recording to ensure data parity.

This process reduces the average enterprise rewrite timeline from 18 months to just a few weeks. Instead of spending 40 hours manually coding a single screen and its associated validation logic, developers spend 4 hours refining the AI-generated output.

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Frequently Asked Questions#

How does Replay handle encrypted backend calls during validation?#

Replay operates at the UI and network interception layer. In many legacy environments, we utilize browser-level or terminal-level hooks to capture data before encryption or after decryption, ensuring that data integrity guarantees validating the payload remain intact without compromising security protocols. For on-premise installations, Replay integrates with your existing security stack to maintain compliance.

Can Replay detect validation rules that happen exclusively on the server?#

While Replay primarily captures the interaction and the data sent/received, our AI Automation Suite can infer server-side constraints by analyzing error responses. If a specific input consistently triggers a 400 Bad Request with a "Invalid Date Range" message, Replay flags this in the "Flows" view, allowing architects to build those data integrity guarantees validating the range into the React frontend.

What happens if the legacy backend changes during the modernization process?#

This is a common challenge in enterprise environments. Because Replay creates a "Blueprint" of the interaction, you can quickly re-record a flow to update your component library. The Replay Library acts as a single source of truth, allowing you to propagate changes across your new React application automatically, maintaining data integrity even as the legacy system evolves.

Does Replay support non-web legacy systems like Citrix or Mainframe terminals?#

Yes. Replay's Visual Reverse Engineering technology is designed to work with a variety of legacy interfaces. By analyzing the visual changes and the input streams, Replay can reconstruct the logical flow of even the most "locked-down" environments, ensuring that data integrity guarantees validating the data entry are preserved in the modern web-based output.

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Ready to modernize without rewriting? Book a pilot with Replay