AI Code Translation Validation with Abstract Syntax Trees
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Solution Overview
Problem
The challenge of validating the accuracy and functionality of code translated using artificial intelligence, particularly when migrating from a legacy programming language to a modern one, is complex due to the inherent complexity of programming languages and the difficulty in ensuring the translated code preserves the intended logic and structure.
Innovation Solution
The use of abstract syntax trees (ASTs) to compare and validate AI-generated code by breaking down input and output source code into elements such as if statements, loops, and variable declarations, and identifying functional equivalence through equivalency mapping, allowing for iterative regeneration of code until an acceptable validation score is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI language model is used to translate source code from legacy language to modern language, then productivity is improved, but reliability deteriorates due to difficulty in validating translation accuracy
Solution Approach 1:
The patent introduces abstract syntax trees (ASTs) as an intermediary representation to validate AI-generated code translations. The AST comparison system acts as a mediator between the original code and translated code, providing an objective validation mechanism that ensures translation accuracy while maintaining high productivity benefits of AI-based code migration.
2Reliability
If AST comparison is used to validate AI-generated code, then reliability is improved, but device complexity increases due to need for AST construction and comparison systems
Solution Approach 1:
The patent creates abstract syntax tree copies of both the original code and AI-translated code, then compares these structural representations. This copying approach simplifies validation by working with standardized AST formats rather than raw code, reducing the effective complexity of the validation system while maintaining high reliability.
3Measurement precision
If ASTs are partitioned into subtrees for comparison, then measurement precision is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent divides complex ASTs into smaller subtree components for comparison. This segmentation enables precise validation of individual code structures and functions, improving measurement precision of translation equivalence while managing complexity through modular, hierarchical processing of code structures.
Data Source
AI summary
Validating code generated by artificial intelligence using abstract syntax trees includes generating, by an artificial intelligence (AI) language model, output source code based on input source code; determining an equivalency mapping between a first abstract syntax tree (AST) constructed for the input source code and a second AST constructed for the output source code; and indicating, based on the equivalency mapping, a validation result for the output source code.