Large Language Model Code Transformation with Context-Aware Prompts

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Solution Overview

Problem

Existing code migration systems face challenges in accuracy, efficiency, and navigational complexity due to reliance on error-prone user modifications, computationally intensive methods, and limited input/output sizes, making large-scale code transformations inefficient and prone to errors.

Innovation Solution

A code transformation system utilizing a large language model to generate transformed code snippets by incorporating context and functionality information, reducing user interaction and improving computational efficiency through end-to-end processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional code migration systems use traditional transformation methods, then they can process code transformations, but they suffer from low accuracy and high error rates due to reliance on user modifications

Engineering Contradiction:

Improveaccuracy of code transformationVSAvoiduser interaction complexity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system enables automated code transformation through AI models that independently analyze source code, generate transformation prompts, and produce target code without requiring manual user modifications. The AI model performs self-service by autonomously completing the code migration task end-to-end

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical code transformation processes with an AI-based system that uses large language models to automatically perform code analysis and transformation, substituting human-operated mechanical processes with intelligent automated systems

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If conventional systems use computationally intensive methods for code transformation, then they can achieve thorough analysis, but they consume excessive computational resources and time

Engineering Contradiction:

Improvecode analysis accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system introduces an intermediary prompt structure that mediates between the source code input and the AI model processing. This prompt includes structured context information that enables the AI model to achieve accurate code transformation with reduced computational complexity by providing targeted context rather than requiring exhaustive analysis

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The code transformation process is segmented into distinct components: source code input, context extraction, prompt generation, AI model processing, and target code output. This segmentation allows the system to process only relevant information through the computationally intensive AI model, reducing overall resource consumption

Inventive Principle:

Principle #1Segmentation

3Productivity

If conventional code migration systems process large-scale codebases, then they can achieve comprehensive transformation, but they face navigational complexity and inefficiency

Engineering Contradiction:

Improvecode transformation efficiencyVSAvoidsystem navigational complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The AI-based code transformation system provides a universal solution that handles diverse code migration scenarios through a single integrated platform. The system can transform code across different programming languages, frameworks, and platforms without requiring separate tools or complex navigation between multiple systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system automatically performs end-to-end code transformation without requiring user navigation through multiple steps or interfaces. The AI model autonomously completes the entire transformation process from source code input to target code output, eliminating navigational complexity

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250306870A1Generating transformed code using a large language model

Publication Date: 2025.10.02 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and providing transformed code snippets using a large language model. In particular, the disclosed systems can determine a code snippet from a code, for instance, based on user selection of the code snippet. The disclosed system can analyze the code and/or the code snippet to generate a prompt comprising context. The context contains information about the functionality of the code. The disclosed systems further use a large language model to analyze the code snippet and the prompt comprising the context and generate transformed code. The disclosed systems may provide the transformed code snippet to one or more devices.