Legacy Code Transformation via AI Context Extraction

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Solution Overview

Problem

Legacy code transformation from languages like COBOL to modern languages such as Java or Python is hindered by manual, line-by-line conversion processes that lack comprehensive context understanding, leading to monolithic code structures, increased maintenance costs, and prolonged timelines.

Innovation Solution

The method employs generative AI models, specifically Large Language Models (LLMs), to receive legacy code data and natural language documents, generate domain context and code explanations, extract knowledge, and fine-tune the models to create a natural language specification document, facilitating automated and accurate legacy code transformation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual line-by-line code conversion is used, then transformation accuracy can be maintained, but transformation time and cost increase significantly

Engineering Contradiction:

Improvetransformation accuracyVSAvoidtransformation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical code conversion process with an automated AI-based system. The AI model analyzes legacy COBOL code and automatically generates modernized code in target languages, eliminating the need for manual line-by-line conversion while maintaining transformation accuracy through intelligent code understanding and generation capabilities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service transformation where the AI model autonomously performs code analysis, context understanding, and code generation without requiring extensive manual intervention. The model learns from the legacy codebase and automatically produces transformed code, reducing both time and resource consumption while maintaining quality.

Inventive Principle:

Principle #25Self-service

2Ease of manufacture

If manual code conversion is used, then detailed control over transformation can be maintained, but the process becomes labor-intensive and error-prone

Engineering Contradiction:

Improvetransformation controlVSAvoidprocess complexity

Core Design Contradiction:

Ease of manufactureVSDevice complexity

Solution Approach 1:

The patent replaces complex manual transformation processes with an automated AI system that handles code analysis, context extraction, and generation. This substitution reduces process complexity by eliminating manual coordination between multiple tools and experts, while maintaining detailed control through configurable transformation parameters and review mechanisms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If legacy code is transformed without comprehensive context understanding, then transformation speed increases, but code quality and maintainability deteriorate

Engineering Contradiction:

Improvetransformation speedVSAvoidcode quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent performs preliminary analysis of the legacy codebase to extract comprehensive context information, including business rules, data structures, and dependencies, before performing the actual code transformation. This preliminary action ensures that the AI model has full contextual understanding, enabling both high transformation speed and high code quality in the subsequent generation phase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary layer of abstracted business logic and context models that mediate between the legacy code and the transformed code. This intermediary layer captures comprehensive context relationships and enables the AI model to generate high-quality code that maintains business logic integrity while improving productivity through automated transformation.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Measurement precision

If traditional modernization programs are used, then thorough code analysis can be achieved, but the programs stretch over a decade with low success rate

Engineering Contradiction:

Improvecode analysis thoroughnessVSAvoidmodernization timeline

Core Design Contradiction:

Measurement precisionVSDuration of action of moving object

Solution Approach 1:

The patent replaces the traditional decade-long mechanical modernization process with an accelerated AI-driven approach. The AI model performs comprehensive code analysis and transformation simultaneously, reducing the timeline from years to months while maintaining thoroughness through intelligent contextual analysis and iterative refinement capabilities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250053389A1Method and system for legacy code transformation

Publication Date: 2025.02.13 INFOSYS LTD

AI summary

This disclosure relates to method and system for facilitating legacy code transformation. The method includes receiving legacy code data and natural language document from one or more data sources. Each of the one or more data sources is one of an external data source or an internal data source. Further, the method includes generating a first natural language output based on the legacy code data through a first LLM, and a second natural language output based on the natural language document through a second LLM. Further, the method includes fine-tuning one of the first LLM or the second LLM based on the first natural language output and the second natural language output, through a third LLM. Further, the method includes generating a natural language specification document corresponding to the legacy code data based on the first natural language output and the second natural language output through the third LLM.