Generating GraphQL Schemas from Visual Data Relationship Mapping in Legacy UIs

Legacy systems are the silent inhibitors of enterprise velocity. While your competitors ship features in days, your team is likely spelunking through 15-year-old COBOL-backed Java applets just to understand how a "Customer" record relates to an "Invoice." With a global technical debt mountain reaching $3.6 trillion, the bottleneck isn't just writing new code—it's deciphering the old code.

The traditional path to modernization is a manual, error-prone slog. Developers spend weeks tracing data flows through obfuscated UIs and undocumented databases. Industry experts recommend a "strangler fig" pattern for migration, but you can't strangle what you don't understand. According to Replay’s analysis, 67% of legacy systems completely lack up-to-date documentation, leaving architects to guess at data relationships.

Generating graphql schemas from visual data mapping represents a paradigm shift. Instead of reading broken code, we observe the application in motion. By recording user workflows, we can infer the underlying data graph, turning a visual recording into a production-ready GraphQL schema.

TL;DR: Manual legacy modernization is failing, with 70% of rewrites exceeding timelines. Replay uses Visual Reverse Engineering to convert video recordings of legacy UIs into documented React components and GraphQL schemas. This approach reduces the average time per screen from 40 hours to just 4 hours, offering a 70% average time savings for enterprise modernization projects.

The Crisis of Undocumented Data Relationships#

Most enterprise modernization projects stall during the discovery phase. When you are tasked with generating graphql schemas from a legacy system, you are essentially performing digital archaeology. You have a UI that works, but the API layer is a "black box" of REST endpoints, SOAP envelopes, or direct SQL injections.

The cost of this manual discovery is staggering. Replay has observed that the average enterprise rewrite takes 18-24 months, largely because architects cannot accurately map data dependencies. When you manually attempt to reconstruct a schema, you risk:

1. •Missing Edge Cases: Overlooking optional fields that only appear in specific workflows.
2. •Type Mismatches: Assuming a field is a
   text

   String
   when it’s actually a complex
   text

   Enum
   .
3. •Relationship Blindness: Failing to see the nested relationship between entities because they are fetched via separate, disconnected calls.

Visual Reverse Engineering is the process of capturing the state, structure, and data flow of a running application via video and DOM inspection to automatically generate modern code equivalents.

Learn more about Legacy Modernization Strategy

The Replay Methodology: Mapping Pixels to Types#

How do we go from a video recording to a strictly typed GraphQL schema? The process involves capturing the "intent" of the UI. When a user clicks a "View Details" button and a modal pops up with customer information, Replay captures the data payload rendered in that modal and the relationship to the parent record.

Step 1: Visual Recording of Workflows#

A developer or business analyst records a standard workflow (e.g., "Onboarding a New Client"). Replay’s engine doesn't just record pixels; it records the metadata of the DOM, the network requests, and the state changes.

Step 2: Relationship Inference#

By analyzing the hierarchy of the legacy UI, Replay identifies "Entities" and "Attributes." If a table displays a list of

and clicking a row reveals , the system infers a one-to-many relationship. This is the foundation for generating graphql schemas from visual inputs.

Step 3: Schema Synthesis#

The captured data is passed through an AI-augmented engine that converts these observations into SDL (Schema Definition Language).

Technical Implementation: Generating GraphQL Schemas From Legacy Components#

To understand how this works in practice, let's look at a typical legacy React/JavaScript component that fetches data via an undocumented REST endpoint. We want to convert this into a modern React component powered by a generated GraphQL schema.

The Legacy Starting Point#

Imagine a legacy "User Profile" screen. The code is a mess of

hooks and manual fetch calls.

typescript
// Legacy Component: UserProfile.js
// No types, undocumented API, manual state management
import React, { useState, useEffect } from 'react';

const UserProfile = ({ userId }) => {
  const [data, setData] = useState(null);

  useEffect(() => {
    // Fetching from an undocumented legacy endpoint
    fetch(`/api/v1/legacy/get-user-details?id=${userId}`)
      .then(res => res.json())
      .then(json => setData(json));
  }, [userId]);

  if (!data) return <div>Loading...</div>;

  return (
    <div className="profile-container">
      <h1>{data.usr_fname} {data.usr_lname}</h1>
      <p>Email: {data.contact_email_primary}</p>
      <div className="roles">
        {data.access_levels.map(role => <span key={role.id}>{role.name}</span>)}
      </div>
    </div>
  );
};

The Generated GraphQL Schema#

By recording the interaction with this component, generating graphql schemas from the observed data becomes automated. Replay identifies the fields

, , and the nested array.

graphql
# Generated by Replay Visual Reverse Engineering
type User {
  id: ID!
  firstName: String @mapped(from: "usr_fname")
  lastName: String @mapped(from: "usr_lname")
  email: String @mapped(from: "contact_email_primary")
  accessLevels: [AccessLevel!]! @mapped(from: "access_levels")
}

type AccessLevel {
  id: ID!
  name: String!
}

query GetUserProfile($id: ID!) {
  user(id: $id) {
    id
    firstName
    lastName
    email
    accessLevels {
      id
      name
    }
  }
}

The Modernized React Component#

Once the schema is generated, Replay produces the modernized React component using the new Design System and the generated GraphQL hooks.

tsx
// Modernized Component: UserProfile.tsx
// Type-safe, documented, and using the new Design System
import React from 'react';
import { useQuery, gql } from '@apollo/client';
import { Card, Typography, Badge, Stack } from '@/components/design-system';

const GET_USER_PROFILE = gql`
  query GetUserProfile($id: ID!) {
    user(id: $id) {
      id
      firstName
      lastName
      email
      accessLevels {
        id
        name
      }
    }
  }
`;

interface Props {
  userId: string;
}

export const UserProfile: React.FC<Props> = ({ userId }) => {
  const { loading, error, data } = useQuery(GET_USER_PROFILE, {
    variables: { id: userId },
  });

  if (loading) return <Card loading={true} />;
  if (error) return <Typography color="error">Error loading profile</Typography>;

  const { user } = data;

  return (
    <Card variant="outline" padding="xl">
      <Stack spacing="md">
        <Typography variant="h1">
          {user.firstName} {user.lastName}
        </Typography>
        <Typography variant="body2" color="muted">
          {user.email}
        </Typography>
        <Stack direction="row" spacing="sm">
          {user.accessLevels.map((level: any) => (
            <Badge key={level.id} color="primary">
              {level.name}
            </Badge>
          ))}
        </Stack>
      </Stack>
    </Card>
  );
};

Discover Design System Automation

Why Visual Mapping Beats Code Analysis#

When generating graphql schemas from legacy systems, architects often default to "Static Analysis." They use tools to scan the source code. However, in enterprise environments (Financial Services, Healthcare, Government), the source code is often a labyrinth of abstractions, dependency injection, and dynamic queries that static analysis tools cannot parse.

According to Replay's analysis, static analysis fails to capture 40% of runtime data transformations. By contrast, visual mapping captures the resolved data state. It doesn't matter if the legacy backend is a complex series of microservices or a monolithic mainframe; if the data reaches the UI, Replay can map it.

The Power of the "Flows" Feature#

In the Replay platform, the Flows feature allows architects to see the entire application architecture as a graph. This is where the magic of generating graphql schemas from visual data truly shines. You can see how a "Transaction" entity in the UI relates to a "Merchant" entity, and Replay will automatically suggest the GQL

or logic.

SOC2 and HIPAA Compliance in Modernization#

For regulated industries, security is the primary concern. Manual rewrites often introduce vulnerabilities because developers don't fully understand the legacy security model. Replay is built for these environments, offering SOC2 compliance and HIPAA-ready configurations. You can even run Replay On-Premise, ensuring that sensitive data never leaves your network while you are generating graphql schemas from your legacy applications.

Scaling to the Enterprise: The AI Automation Suite#

Modernizing a single screen is easy. Modernizing 5,000 screens is an existential threat to an IT budget. Replay’s AI Automation Suite scales the process of generating graphql schemas from visual recordings across entire portfolios.

1. •Batch Recording: Record hundreds of user sessions across different modules.
2. •Schema Merging: The AI identifies duplicate entities (e.g., "Client" in the Billing module vs. "Customer" in the CRM module) and merges them into a unified GraphQL schema.
3. •Conflict Resolution: When two legacy UIs represent the same data differently, Replay flags the discrepancy for an architect to review in the Blueprints editor.

This automation is what allows Replay to boast a 70% average time savings. What used to take 18 months now takes weeks.

Best Practices for Generating GraphQL Schemas from Visual Data#

To get the most out of Replay, follow these industry-standard recommendations:

1. Focus on High-Value Workflows#

Don't try to map every single obscure setting page. Start with the core business workflows. Recording these first ensures that your generated GraphQL schema covers the most critical 80% of your data needs.

2. Validate with the "Blueprints" Editor#

Replay's Blueprints feature allows you to tweak the generated code and schema before it's finalized. Use this to refine naming conventions (e.g., changing

to ) to ensure the new system follows modern clean code standards.

3. Implement an Incremental Migration#

Don't flip a switch. Use the generated GraphQL schema to build a "BFF" (Backend for Frontend) layer. This allows your new React components to fetch data from the legacy system via GraphQL while you slowly migrate the backend database.

The Future of Visual Reverse Engineering#

We are moving toward a world where the "Source Code" is no longer the only source of truth. The "User Experience" is becoming the primary driver for architectural design. By generating graphql schemas from the UI, we are acknowledging that the way users interact with data is how that data should be structured in a modern stack.

As technical debt continues to grow—currently estimated at $3.6 trillion globally—the companies that survive will be those that can modernize without being buried by the cost of manual rewrites. Replay provides the bridge from the legacy past to the typed, componentized future.

Frequently Asked Questions#

How does Replay handle complex data relationships that aren't visible on a single screen?#

Replay uses a multi-session mapping approach. By recording multiple related workflows, the platform's Flows engine correlates data entities across different screens. For example, if a "User ID" appears in both a "Search" workflow and a "Settings" workflow, Replay recognizes them as the same entity and builds a unified GraphQL Type.

Can Replay generate schemas from legacy systems that don't have a web UI?#

Replay is primarily a Video-to-code platform optimized for web-based legacy UIs (including older frameworks like AngularJS, Backbone, or even server-side rendered JSP/ASP.NET). For terminal-based or thick-client applications, Replay offers specialized integration paths, but the visual reverse engineering is most powerful on DOM-based applications.

What happens if the legacy UI has inconsistent data naming?#

This is a common issue where one screen calls a field

and another calls it . Replay’s AI Automation Suite identifies these as likely candidates for the same field based on data format and context. It then presents these to the architect in the Blueprints editor to confirm a single, unified name for the generated GraphQL schema.

Is the generated React code and GraphQL schema production-ready?#

Yes. While we always recommend a brief architectural review, the code generated by Replay follows modern best practices, including TypeScript definitions, clean component separation, and optimized GraphQL queries. According to Replay's analysis, the generated code reduces manual refactoring time by over 80%.

How does Replay ensure security during the recording process?#

Replay is built for regulated environments like Financial Services and Healthcare. It includes built-in PII (Personally Identifiable Information) masking that prevents sensitive data from being captured during the recording phase. Furthermore, Replay is SOC2 compliant and offers an On-Premise deployment option for maximum data sovereignty.

Ready to modernize without rewriting? Book a pilot with Replay