Retrieval-Augmented Code Translation With Structured Metadata
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Solution Overview
Problem
Existing code translation methods, including off-the-shelf and fine-tuned language models, fail to provide accurate translations due to the challenges of understanding both syntax and semantics in programming languages.
Innovation Solution
A method involving a database of code samples with indexed metadata, including intermediate representations and abstract syntax trees, is used to generate prompts for pre-trained language models to translate code accurately by leveraging similarity-based retrieval and iterative correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If off-the-shelf language models are used for code translation, then the translation process is simple, but translation accuracy is poor
Solution Approach 1:
The system performs preliminary actions by generating metadata (including abstract syntax trees and intermediate representations) for both input and reference code samples before the translation process. This preprocessing enables the retrieval-augmented generation to access structured code representations, improving translation accuracy without complicating the core translation mechanism
Solution Approach 2:
The system introduces an intermediary retrieval database that stores metadata from code samples. This intermediary structure mediates between the input code and the language model, allowing the model to query relevant code patterns and translations, thereby improving accuracy while keeping the language model itself simple and unchanged
2Measurement precision
If fine-tuned models are used for code translation, then translation capability is improved, but computational complexity increases
Solution Approach 1:
The system segments the translation task into distinct components: metadata generation from input code, similarity-based retrieval of reference samples, and translation generation. This segmentation allows using a simple pre-trained language model without fine-tuning, reducing computational complexity while maintaining accuracy through the structured retrieval-augmented approach
Solution Approach 2:
The system enables self-service by using the language model in its pre-trained state without requiring fine-tuning. The retrieval-augmented generation mechanism allows the model to serve itself by querying the database for relevant code patterns, eliminating the need for complex fine-tuning processes while maintaining translation accuracy
3Measurement precision
If metadata generation and code sample retrieval are implemented, then translation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-generating and storing metadata (abstract syntax trees, intermediate representations) for code samples in a database before translation is needed. This preprocessing step enables fast retrieval during actual translation, reducing processing time while maintaining high translation accuracy through the use of pre-computed structured representations
Data Source
AI summary
Methods and systems for code translation include generating metadata for input program code, including an intermediate representation of the input program code. A database of stored code samples is searched to select an example code sample based on similarity between metadata of the input program code and stored metadata of the stored code samples. A prompt is generated that includes the input program code, the example code sample, and a translation of the example code sample. The prompt is applied to a pre-trained language model to generate a translation of the input program code in a target programming language.