AI-Generated Code Translation Accuracy via Cross-Compilation

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Solution Overview

Problem

Migrating legacy source code to a modern programming language using artificial intelligence can introduce errors, leading to potential system failures and outages due to the arduous task of writing, testing, and debugging large amounts of code, especially in critical production systems.

Innovation Solution

Utilize cross-compilation with a generative artificial intelligence model to convert code between programming languages, calculate complexity scores using metrics like cyclomatic complexity and Halstead metrics, and determine a translation accuracy score by comparing original and converted code, updating the model based on accuracy scores to ensure fidelity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If AI-generated code is used to migrate legacy source code to modern programming languages, then productivity and maintainability are improved, but translation accuracy deteriorates due to potential errors in code generation

Engineering Contradiction:

Improvecode migration speedVSAvoidtranslation accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary cross-compilation of the AI-generated code before deployment to detect potential translation errors. By converting the code back to the original programming language and comparing complexity metrics, the system identifies issues beforehand, preventing faulty code from entering production systems.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system establishes a feedback loop where cross-compilation results and complexity score comparisons are used to validate AI-generated code. The feedback mechanism allows continuous monitoring and adjustment of translation quality, ensuring that only accurate code is deployed while maintaining high productivity.

Inventive Principle:

Principle #23Feedback

2Reliability

If comprehensive testing and validation are performed on migrated code, then translation accuracy is improved, but time consumption increases due to arduous testing processes

Engineering Contradiction:

Improvecode validation accuracyVSAvoidtesting duration

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system replaces manual, time-consuming testing processes with automated cross-compilation and complexity metric comparison. By using computational methods to validate code translation accuracy, the system achieves comprehensive validation without the time penalty of traditional manual testing approaches.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Manufacturing precision

If complex validation processes are implemented, then code quality is improved, but device complexity increases due to additional validation tools and processes

Engineering Contradiction:

Improvecode qualityVSAvoidvalidation system complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The cross-compilation system serves multiple functions: it validates translation accuracy, compares complexity metrics, and ensures code quality all within a single integrated process. By making the validation system multi-functional, the patent reduces overall device complexity while maintaining high code quality standards.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250217127A1Using cross-compilation to determine translation accuracy of artificial intelligence generated code

Publication Date: 2025.07.03 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Using cross-compilation to determine translation accuracy of artificial intelligence generated code includes receiving a first code portion of a first programming language, converting the first code portion to a second code portion of a second programming language by a generative artificial intelligence model, and converting the second code portion to a third code portion of the first programming language by the generative artificial intelligence model. A translation accuracy score of the converting of the first code portion to the second code portion is calculated.