Neural Code Translation from APL to C# Revolutionizes Legacy Code

Abdulrahman Ramadan and collaborators have developed a novel framework for translating legacy APL code into C# using neural language models. Their research addresses the complexities of APL's sparse syntax and the challenges presented by a lack of large-scale parallel datasets. By comparing three guided strategies—natural language description-mediated, retrieval-augmented, and iterative refinement—against a baseline direct translation model, the authors constructed multiple datasets of functionally equivalent code pairs. They employed an automated evaluation pipeline to rigorously assess the translation quality, focusing on both syntactic compilation and functional execution of the generated C# code.

The results indicate that neural code translation can effectively bridge the gap between APL and C# for a variety of applications. Notably, the incorporation of additional context and guidance significantly enhances model performance, suggesting that such approaches could be applied to other programming language translations as well. This advancement not only aids in the modernization of legacy systems but also streamlines the process for developers, allowing for easier maintenance and adaptation of older codebases to current programming environments. The implications of this research are profound, potentially impacting software engineering practices across industries.