Neural Transformer Code Translation via Attention and Beam Search

The patent replaces the mechanical system of hand-crafted rewrite rules with a neural transformer model that learns translation patterns automatically from data. The model uses attention mechanisms to capture contextual relationships between source and target code, eliminating the need for explicit rule-based translation systems while maintaining or improving translation quality across multiple programming languages.

The neural transformer model provides a universal translation framework that can handle multiple source-target language pairs with a single system. Unlike rule-based approaches that require separate rule sets for each language pair, the model learns generalizable translation patterns from training data and can translate between any supported language combination without requiring additional rule development.

The patent implements a dynamic translation system where the neural model adapts its translation strategy based on the input code characteristics, source and target language properties, and contextual information. The attention mechanism dynamically weights different parts of the input sequence, allowing the system to flexibly handle diverse programming language grammars and syntax structures without requiring pre-configured rules for each language combination.

The model performs preliminary learning from large-scale training data containing multiple programming languages and their translations. This pre-training enables the system to acquire knowledge about different language grammars, syntax patterns, and translation conventions before actual translation tasks, improving its adaptability to new language pairs while maintaining operational control through the trained parameters.

The neural transformer model performs self-learning from training data without requiring explicit programming or expert intervention for each language pair. The system automatically acquires translation knowledge by processing large corpora of source and target code, eliminating the need for domain experts to manually create translation rules while maintaining high translation quality through learned patterns and contextual understanding.

Extensive training on diverse programming language pairs is performed in advance to pre-load the model with translation knowledge. This preliminary action captures expert-level translation patterns during the training phase, allowing the deployed system to perform high-quality translations without requiring ongoing expert involvement or manual rule creation for each new language combination.