Syntax-Tree Code Synthesis with LLM Feedback for Accurate Translation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing code translation methods struggle with maintaining functional integrity and semantic nuances when converting between programming languages, often resulting in inaccurate, inefficient, and non-idiomatic translations.

Innovation Solution

A system utilizing abstract syntax trees, dependency graphs, and large language models to iteratively refine code translations, incorporating human feedback for enhanced accuracy and reliability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If direct syntax conversion is used for code translation, then translation speed is improved, but translation accuracy and functional integrity deteriorate

Engineering Contradiction:

Improvetranslation speedVSAvoidtranslation accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent segments the code translation process into multiple stages: parsing source code into abstract syntax trees, generating intermediate representations, and synthesizing target code. This multi-stage approach allows for better preservation of semantic information while maintaining translation efficiency, resolving the contradiction between speed and accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediate representation (IR) as a mediator between source and target languages. The IR captures semantic information in a language-agnostic form, allowing accurate translation while maintaining efficiency. This intermediary structure prevents direct syntax conversion errors while avoiding the slowness of manual translation.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If traditional code translation methods are used, then simplicity of the translation process is improved, but handling of context-sensitive information deteriorates

Engineering Contradiction:

Improvetranslation process simplicityVSAvoidcontext-sensitive information

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The patent performs preliminary analysis of the source code to extract context-sensitive information such as variable scoping, type inference, and semantic relationships before translation. This preliminary action ensures that contextual information is preserved and properly handled during the translation process, while maintaining a systematic and manageable approach.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent adds a semantic dimension to the translation process by incorporating abstract syntax trees and intermediate representations that capture contextual information. This additional dimension allows the system to handle context-sensitive information without significantly increasing overall process complexity, as the semantic layer operates independently from syntax conversion.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Productivity

If automated code generation is used, then productivity is improved, but code quality and reliability deteriorate

Engineering Contradiction:

Improveautomated code generation speedVSAvoidcode quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent incorporates feedback mechanisms where the generated target code is validated against the original semantic representation. The system checks whether the translation preserves functional integrity and contextual information, and iteratively refines the translation if discrepancies are found. This feedback loop maintains high code quality while preserving automated generation efficiency.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary validation and semantic verification during the translation process, before final code generation. By checking semantic consistency and functional equivalence in advance, the system prevents generation of low-quality code, ensuring reliability while maintaining automated productivity through early error detection and correction.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

Data Source

PatentUS20250306882A1Systems and methods for iterative feedback-driven code synthesis using syntax trees and large language models

Publication Date: 2025.10.02 UST GLOBAL INC

AI summary

A system for translating source code in a first programming language to a target language is provided. The system is configured to receive source code for converting to target code; determine an abstract syntax tree from the source code; determine program specifications from the source code; determine a dependency graph from the source code; determine a plurality of chunks based at least in part on the abstract syntax tree, the program specifications, and the dependency graph; determine a plurality of converted chunks based at least in part on the plurality of chunks and a deep learning model, the deep learning model converting the plurality of chunks from the language of the source code to the language of the target code; post-process the plurality of converted chunks to obtain intermediate code; and provide the intermediate code as the target code.