Context-Aware Code Translation via IR and LLM
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Solution Overview
Problem
Existing code translation methods face challenges such as the need for large task-specific datasets, poor generalization, and the loss of language-specific properties during translation, leading to semantic gaps and readability issues.
Innovation Solution
A computer-implemented method and system that efficiently translates program code by parsing source code into an Intermediate Representation (IR), establishing a structural and semantic model, and using a Large Language Model (LLM) to translate ordered tasks into a target language, while maintaining context-awareness and coherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional SSA-based IR is used for code translation, then data flow analysis efficiency is improved, but language-specific properties and semantic integrity are lost
Solution Approach 1:
The patent segments the code translation process into distinct phases: parsing source code to IR, transforming IR to SSA form for analysis, then reconstructing the target code while preserving semantic information. This segmentation allows efficiency gains from SSA without permanently losing language-specific properties, as the reconstruction phase restores them.
Solution Approach 2:
The patent introduces an intermediary representation that bridges SSA form and target code by preserving semantic information during transformation. This intermediary structure acts as a mediator that maintains language-specific properties while benefiting from SSA-based data flow analysis efficiency.
2Adaptability or versatility
If LLMs are used for in-context learning in code translation, then natural language processing capability is improved, but token overflow and incorrect translation occur
Solution Approach 1:
The patent applies preliminary static analysis to the IR before LLM translation, establishing structural and semantic models that constrain and guide the LLM's in-context learning. This preliminary preparation prevents token overflow and ensures translation accuracy by providing a solid foundation for the LLM to work within.
Solution Approach 2:
The patent implements feedback mechanisms where the structured IR and semantic models provide continuous guidance to the LLM during translation. This feedback loop ensures that the LLM's natural language processing capabilities are harnessed effectively while maintaining translation accuracy and preventing errors.
3Reliability
If traditional fine-tuning with gradient descent is used, then model convergence is achieved, but large task-specific datasets are required
Solution Approach 1:
The patent uses IR and semantic models as intermediaries that encode task-specific knowledge, replacing the need for large annotated datasets. These intermediaries serve as compact representations that guide the translation process without requiring extensive fine-tuning data.
Solution Approach 2:
The patent substitutes the mechanical gradient descent fine-tuning process with a more efficient approach using pre-trained LLMs guided by structured IR representations. This substitution achieves model convergence without the resource-intensive process of traditional fine-tuning on large datasets.
4Productivity
If conventional transpilation methods are used, then code translation is achieved, but semantic gaps and readability issues arise
Solution Approach 1:
The patent performs preliminary static analysis and establishes semantic models before translation, ensuring that semantic integrity is preserved throughout the process. This preliminary preparation prevents semantic gaps while maintaining translation efficiency.
Solution Approach 2:
The patent introduces semantic models as intermediaries between source and target code that preserve language-specific properties and semantic information. These intermediaries act as bridges that maintain readability and semantic integrity while enabling efficient translation.
Data Source
AI summary
Systems and methods for efficiently translating program code from a source language to a target language. Input source code is parsed, using a processor device, into an Intermediate Representation (IR). A structural and semantic model of the source code are established by applying static analysis to the IR, and a program skeleton of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a Single Static Assignment (SSA) form, and a System Dependency Graph (SDG) is built from the SSA form. The SDG is traversed to order translation tasks, and ordered tasks are translated into the target language using a Large Language Model (LLM). A translated program is generated by integrating translated code segments into a coherent program structure in the target language.