Localization Extraction Patterns: Discovering 1,000+ Hardcoded Strings via Visual Behavioral Analysis

Static analysis is a lie when it comes to legacy UI modernization. If you rely solely on

or Regex to find hardcoded strings in a 15-year-old monolithic application, you are missing approximately 40% of your translation keys. Legacy systems don't just store strings in variables; they concatenate them across functions, fetch them from undocumented stored procedures, and inject them into the DOM via antiquated state machines.

When your enterprise is facing a $3.6 trillion global technical debt, the manual approach to internationalization (i18n) is a death march. For a typical 200-screen enterprise application, manual string extraction takes an average of 40 hours per screen. With Replay, that timeline collapses from 18 months to a few weeks.

TL;DR: Manual localization audits are prone to 40%+ error rates due to dynamic string concatenation and "ghost" strings. By utilizing localization extraction patterns discovering via visual behavioral analysis, teams can automate the identification of hardcoded text by recording user workflows. This "Video-to-code" approach reduces modernization timelines by 70%, turning months of manual audit into days of automated discovery.

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The Failure of Static Analysis in Legacy i18n#

Most legacy systems—built in Delphi, PowerBuilder, or early .NET—lack any semblance of a design system or centralized localization layer. According to Replay’s analysis, 67% of legacy systems lack documentation entirely, leaving architects to hunt for strings like "Submit," "Error: Invalid Entry," or "System Timeout" across millions of lines of spaghetti code.

The problem with traditional localization extraction patterns discovering via static code analysis is "Semantic Fragmentation." A single sentence in the UI might be composed of three different database calls and two hardcoded prefixes.

Video-to-code is the process of recording a user's interaction with a legacy application and using computer vision and metadata interception to transform those visual elements into documented, modern React components.

By recording the application in a runtime state, we capture the final rendered output. We aren't looking at the code; we are looking at the behavior. This allows us to identify strings that only appear under specific conditional logic—strings that regex would never find because they don't exist in the source code as a single contiguous unit.

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Advanced Localization Extraction Patterns: Discovering Strings in Legacy UIs#

To successfully automate the migration to a modern i18n framework like

or , we must categorize how strings hide within legacy architectures. We have identified four primary patterns that emerge during the visual analysis phase.

1. The Concatenated Fragment Pattern#

In legacy systems, developers often saved memory or "reused" words to build sentences. Example:

Static analysis sees four separate strings. Visual analysis sees the complete semantic meaning, allowing for a single translation key with interpolation.

2. The Hidden State Pattern#

These are strings that only appear during specific error states or edge cases (e.g., "Database connection failed in Region B"). Since 70% of legacy rewrites fail or exceed timelines, missing these edge-case strings is often what triggers a "re-work" cycle six months into a project.

3. The Image-Embedded Text Pattern#

Legacy UIs frequently use buttons or icons where the text is baked into a

or file. No text-based scanner will ever find "Save" if it's a collection of pixels. Replay’s visual reverse engineering identifies these elements as functional components, flagging them for replacement with CSS-based localized buttons.

4. The Stored Procedure Payload#

In many "Fat Client" architectures, the UI is a thin shell for SQL logic. The strings live in the database. By recording the flow, we capture the payload as it hits the UI, regardless of its origin.

Learn more about visual reverse engineering

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Comparison: Manual vs. Automated Extraction#

The following table demonstrates the efficiency gains when moving from manual regex-based discovery to automated localization extraction patterns discovering via Replay.

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Implementing the Extraction: A Technical Deep Dive#

When we use Replay to record a workflow, the platform doesn't just "film" the screen. It intercepts the metadata of the UI elements. This allows us to map a hardcoded string in a legacy Java Applet directly to a modern React component.

Industry experts recommend a "Bottom-Up" approach to string extraction. Instead of trying to find every string at once, you record specific "Flows"—such as "Create New Account" or "Process Insurance Claim."

Step 1: Capturing the Legacy Flow#

Using Replay, an analyst records the "Claims Processing" flow. Every tooltip, error message, and button label is captured in its "natural habitat."

Step 2: Extracting to a JSON Translation Map#

Once the flow is recorded, Replay’s AI Automation Suite identifies the text nodes. Below is an example of the "raw" legacy output vs. the modernized React component generated by Replay.

Legacy Code (Conceptual):

typescript
// Legacy .NET / WinForms style logic found in the "black box"
public void ShowStatus(int code) {
    if (code == 1) {
        label1.Text = "Processing..."; // Hardcoded
    } else {
        label1.Text = "Error # " + code + " has occurred."; // Concatenated
    }
}

Modernized React Component (Generated by Replay):

tsx
import React from 'react';
import { useTranslation } from 'react-i18next';

interface StatusDisplayProps {
  statusCode: number;
}

/**
 * Component extracted via Replay Visual Analysis.
 * Original Flow: Insurance Claims Dashboard -> Status Update
 */
export const StatusDisplay: React.FC<StatusDisplayProps> = ({ statusCode }) => {
  const { t } = useTranslation();

  return (
    <div className="status-container">
      <span className="status-text">
        {statusCode === 1 
          ? t('status.processing', 'Processing...') 
          : t('status.error', { 
              defaultValue: 'Error # {{code}} has occurred.', 
              code: statusCode 
            })
        }
      </span>
    </div>
  );
};

By utilizing localization extraction patterns discovering, Replay automatically suggests the

keys and creates the corresponding file. This eliminates the manual labor of building the translation dictionary.

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Measuring the ROI of Localization Extraction Patterns Discovering#

The average enterprise rewrite timeline is 18 months. A significant portion of that time is spent on "Discovery"—simply understanding what the current system does. When you factor in the 67% of systems that lack documentation, you realize that architects are essentially archeologists.

According to Replay’s analysis, using visual behavioral analysis to drive localization extraction patterns discovering saves an average of 70% in total modernization time.

Why Visual Analysis Beats AST Parsing#

Abstract Syntax Tree (AST) parsing is great for modern JavaScript, but it fails on legacy binaries or obfuscated code. Visual analysis is "language agnostic." Whether the legacy app is written in COBOL, Smalltalk, or a proprietary internal language, the pixels on the screen don't lie.

If the user sees "Account Balance," that is a string that needs extraction. Replay's Flows feature allows you to map these visual strings to specific architectural components, ensuring that your new React frontend maintains 100% parity with the legacy business logic.

Read about component library automation

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Scaling to 1,000+ Strings: The Automation Suite#

When dealing with 1,000+ strings, manual verification is impossible. Replay’s AI Automation Suite uses heuristic-based matching to group similar strings.

For instance, if "Submit," "SUBMIT," and "submit" appear across 50 different screens, Replay identifies these as a single localization extraction pattern discovering event. It suggests a global

key rather than 50 individual entries.

Example: The i18n Resource Bundle#

Replay generates a structured resource bundle that is ready for a Translation Management System (TMS) like Phrase or Lokalise.

json
{
  "dashboard": {
    "header": "Enterprise Resource Planner",
    "welcome_message": "Welcome back, {{name}}",
    "last_login": "Your last login was on {{date}}"
  },
  "actions": {
    "save": "Save Changes",
    "cancel": "Discard",
    "delete_confirm": "Are you sure you want to delete this record?"
  },
  "errors": {
    "auth_failed": "Authentication failed. Please check your credentials.",
    "timeout": "The server took too long to respond. (Code: 504)"
  }
}

This level of organization is achieved automatically because Replay understands the context of where the string was found. A "Save" button in a Modal is categorized differently than a "Save" link in a Footer.

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Overcoming Regulated Industry Hurdles#

For Financial Services, Healthcare, and Government sectors, modernization isn't just about speed—it's about compliance. Replay is built for these environments, offering SOC2 compliance, HIPAA-readiness, and On-Premise deployment options.

When performing localization extraction patterns discovering, Replay ensures that PII (Personally Identifiable Information) is masked during the recording process. This allows architects to analyze workflows and extract hardcoded UI strings without ever seeing sensitive customer data.

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Frequently Asked Questions#

How does visual analysis find strings that aren't currently on the screen?#

Visual behavioral analysis relies on "Flow Coverage." While it cannot see a string that is never rendered, Replay's automation tools allow developers to script "brute-force" interactions—triggering every error message and dropdown menu—to ensure 100% visual coverage. This is still 10x faster than reading through millions of lines of dead code to find which strings are actually active.

Can Replay handle right-to-left (RTL) languages during extraction?#

Yes. Because Replay captures the visual bounding boxes of UI elements, it identifies the spatial relationship of text. When modernizing a legacy LTR (Left-to-Right) system to support RTL (like Arabic or Hebrew), Replay flags components that require directional CSS logic, not just text translation.

What happens to strings concatenated at runtime?#

This is where localization extraction patterns discovering shines. Replay's metadata interception sees the final string sent to the browser's DOM or the application's paint engine. It then uses AI to "reverse-engineer" the likely variables (like names or dates) to create an i18next-style template with placeholders automatically.

Does this replace the need for a professional translator?#

No. Replay automates the extraction and implementation of the localization framework. It identifies where the strings are and prepares the code to receive translations. You still need a professional to provide the actual localized text for the target languages, but they will be working with a clean, organized JSON file instead of digging through source code.

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Conclusion: The Path to a Globalized Architecture#

Modernizing a legacy monolith is an exercise in risk management. The greatest risk is the "Unknown Unknown"—the hardcoded string that breaks the layout in the French version because it was never identified during discovery.

By shifting from manual audits to localization extraction patterns discovering via Replay, enterprise architects can guarantee 100% visibility into their UI's text layer. You aren't just moving code; you are documenting the behavioral intent of your application and transforming it into a clean, modern, and globally-ready React architecture.

Ready to modernize without rewriting? Book a pilot with Replay