Translating code with Large Language Models and human-in-the-loop feedback

doi:10.1016/j.infsof.2025.107785

Translating code with Large Language Models and human-in-the-loop feedback

2025-05-31

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Gabriele Dario De Siano

DOI：10.1016/j.infsof.2025.107785

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Abstract

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• We investigate the use of three Generative AI tools from the human perspective. • We focus on translation tasks from query language code to framework-specific code. • We evaluate the tools’ usefulness, the quality of the code, and their performance. • Our study involves 15 participants with diverse backgrounds in three usage scenarios. • We establish a methodology and metrics for evaluating generative code translation.

Keywords:

Large Language Model

Human-centered AI

Code translation

Software engineering

Translating code with Large Language Models and human-in-the-loop feedback

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