Hybrid Feedback Transpiler for Code Translation Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large organizations face challenges in efficiently, effectively, and uniformly managing computer systems that exchange information, particularly during platform migrations, due to residual code and technical debt, and existing language conversion technologies are error-prone and biased.
Innovation Solution
A bi-directional hybrid-feedback driven self-healing and self-scaling language transpiler system that uses natural language processing and machine learning to automatically translate code between languages, facilitating self-correction and scaling, with a hybrid feedback mechanism to update mappings and vocabulary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If brute force language substitution is used for code translation, then translation speed is improved, but translation accuracy deteriorates due to error proneness and inability to learn from errors
Solution Approach 1:
The patent implements feedback mechanisms where translation errors are detected and fed back into the system to improve future translations. The system learns from its mistakes by analyzing incorrect translations and adjusting its translation rules accordingly, thereby improving accuracy while maintaining automated translation capability.
Solution Approach 2:
The translation system performs self-correction by automatically identifying and fixing its own translation errors without external intervention. The system monitors its translation output, detects inconsistencies and errors, and autonomously adjusts its translation model to prevent recurring mistakes.
2Adaptability or versatility
If general models with large amounts of data are used, then model coverage is improved, but biasing errors increase due to dataset characteristics
Solution Approach 1:
The patent applies local quality by using domain-specific translation models trained on targeted datasets rather than general-purpose models. Each translation model is specialized for particular programming languages or domains, ensuring higher accuracy within specific contexts while maintaining overall system versatility through multiple specialized models.
Solution Approach 2:
The system dynamically adjusts model parameters and selection based on the specific translation task at hand. By changing which model is used or how it is configured based on the source and target languages involved, the system achieves both broad coverage and high accuracy for each specific translation scenario.
3Reliability
If learned biasing errors are maintained in the model, then model performance on training data is improved, but model usability deteriorates outside the learned environment
Solution Approach 1:
The patent implements dynamic model adaptation where the translation system can adjust its behavior based on the input context. The model is not static but can dynamically switch between different translation strategies or models depending on the source language, target language, and domain specificity of the code being translated.
Solution Approach 2:
The system achieves universality by creating a framework that can handle multiple programming languages and translation scenarios through a collection of specialized models. Rather than being limited to a single learned environment, the system provides broad usability across different programming contexts while maintaining high performance in each specific domain.
4Manufacturing precision
If manual code regeneration is performed on new platforms, then code accuracy is improved, but time consumption and operational costs increase
Solution Approach 1:
The patent applies preliminary action by performing automated translation and validation before actual platform migration. The system pre-translates code from legacy platforms to new platforms, allowing thorough testing and validation of the translated code before the migration is finalized, thereby reducing the time needed during actual migration while maintaining accuracy.
Solution Approach 2:
The system replaces manual mechanical code regeneration processes with automated intelligent translation. Instead of requiring developers to manually rewrite and adapt code on new platforms, the AI-powered translation system automatically performs the conversion, maintaining code accuracy through learned patterns while dramatically reducing the time and labor required.
Data Source
AI summary
Various aspects of the disclosure relate to bi-directional hybrid-feedback driven self-healing and self-scaling language transpiler system may include bi-directional hopping to support multi language transpilation, automatic conversion of a mapping into a transformation specification, a hybrid feedback mechanism to update the transformation mappings, automatic scaling and/or creation of enterprise wide mapping and token (e.g., grammar) vocabulary, and/or a self-healing and/or corrective translation capability to perform automatic correction of any partial transpilations over time from a learned mapping.