LLM Code Translation With Concurrent Output Validation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Translating legacy source code to modern programming languages is labor-intensive and error-prone, particularly in critical systems like government and financial systems, where inaccuracies can have severe consequences.

Innovation Solution

Concurrently execute the original and translated programs to generate equivalent outputs, using a language model to correct discrepancies, and update the model based on metadata generated from output comparisons.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual translation of legacy source code to modern programming languages is performed, then translation accuracy can be maintained through human expertise, but the process becomes extremely labor-intensive and time-consuming

Engineering Contradiction:

Improvetranslation accuracyVSAvoidtranslation speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical translation processes with an automated language model system that uses machine learning to translate legacy code to modern programming languages, eliminating the need for human programmers to manually rewrite code while maintaining high translation accuracy through the model's learning capabilities

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The language model performs self-improvement by automatically learning from translation discrepancies detected during concurrent execution of original and translated programs, correcting its own errors without human intervention, and continuously refining translation accuracy through self-service training on detected errors

Inventive Principle:

Principle #25Self-service

2Productivity

If automated language models are used for code translation, then translation speed and productivity are greatly improved, but translation accuracy and reliability deteriorate due to potential errors in generated code

Engineering Contradiction:

Improvetranslation speedVSAvoidtranslation reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements a feedback mechanism where the language model's translations are automatically validated by comparing outputs from concurrent execution of original and translated programs, with detected discrepancies fed back to the model for correction and retraining, creating a closed-loop system that continuously improves translation reliability

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary validation of translated code by instrumenting both original and translated programs with comparison regions that generate outputs at equivalent points, allowing errors to be detected and corrected before the translated code is deployed to production systems

Inventive Principle:

Principle #10Preliminary action

3Productivity

If translated code is deployed directly without validation, then productivity is maximized by immediate deployment, but harmful factors increase due to undetected errors in critical systems

Engineering Contradiction:

Improvedeployment speedVSAvoiderror propagation in critical systems

Core Design Contradiction:

ProductivityVSObject-generated harmful factors

Solution Approach 1:

The patent performs preliminary validation of translated code through concurrent execution and output comparison before deployment, instrumenting both original and translated programs to detect errors in advance, ensuring that only validated translations are deployed to critical systems

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent takes preliminary anti-action by detecting and correcting translation errors before they can propagate into critical systems, using the feedback loop to identify and fix discrepancies in the translated code, thereby preventing potential harmful effects from occurring

Inventive Principle:

Principle #9Preliminary anti-action

Data Source

PatentUS20260044327A1LLM-based code translation and virtual deployment

Publication Date: 2026.02.12 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Methods and systems for code translation include translating source code for an original program, written in a first programming language, to source code for a translated program, written in a second programming language, using a language model. The original program and the translated program are instrumented, using comparison regions of each, to configure the original program and the translated program to generate respective outputs at equivalent points. The original program and the translated program are executed concurrently to generate an original output and a translated output. The language model is updated to correct the translated program based on a discrepancy between the original output and the translated output.