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Analysis of Implementation Pathways for Generative AI in Cross-language Code Migration and Compatibility Testing
TOTAL VIEWS: 110
Abstract
Cross-language code migration and compatibility testing are critical processes in modern software engineering to enhance system maintainability and scalability. Generative artificial intelligence demonstrates unique advantages in code comprehension, semantic analysis, and automated generation. By leveraging large-scale language models to semantically abstract source code and map it to target languages, this study explores its potential to improve migration accuracy, reduce manual intervention, and ensure system compatibility. Experiments indicate that this technology can optimize migration workflows, enhance code consistency and test coverage, and provide effective support for the stable operation of software systems in multi-language environments. The findings highlight that generative AI offers a novel technical approach and methodological support for cross-language migration and compatibility testing.
Keywords
Artificial Intelligence; Code Migration; Compatibility Testing; Software Engineering
References
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How to cite this paper
Analysis of Implementation Pathways for Generative AI in Cross-language Code Migration and Compatibility Testing
How to cite this paper: Zhenlin Jin. (2025) Analysis of Implementation Pathways for Generative AI in Cross-language Code Migration and Compatibility Testing. Advances in Computer and Communication, 6(4), 256-261.
DOI: http://dx.doi.org/10.26855/acc.2025.10.017
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