Bridging Eras: Transforming Fortran Legacies into Python']Python with the Power of Large Language Models

被引:1
|
作者
Pietrini, Rocco [1 ]
Paolanti, Marina [1 ]
Frontoni, Emanuele [1 ]
机构
[1] Univ Politecn Marche, VRAI, Ancona, Italy
来源
2024 IEEE 3RD INTERNATIONAL CONFERENCE ON COMPUTING AND MACHINE INTELLIGENCE, ICMI 2024 | 2024年
关键词
AI; Fortran; !text type='Python']Python[!/text; Translation; Legacy Code; LLM;
D O I
10.1109/ICMI60790.2024.10586058
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Embarking on the challenging expedition from Fortran's venerable era to the dynamic landscape of Python presents a formidable task in the world of legacy code. This case study intricately navigates the transformative journey of translating a 1970s software, marked by scant documentation and challenging maintainability, into Python, employing the prowess of Large Language Model (LLM). Through a detailed exploration of the encountered nuances, pitfalls, and triumphs, this article vividly illustrates a real-world application of state-of-the-art language models in the realm of software modernization. Our experimentation with cutting-edge LLMs on authentic code reveals that while challenges persist, these models serve as invaluable tools for expediting the modernization process. This endeavor not only promises to breathe new life into aged software but also underscores its tangible societal and practical impact across top-tier industries.
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页数:5
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