@inproceedings{ranasinghe-etal-2025-llm,
title = "{LLM}-Assisted Translation of Legacy {FORTRAN} Codes to {C}++: A Cross-Platform Study",
author = "Ranasinghe, Nishath Rajiv and
Jones, Shawn M. and
Kucer, Michal and
Biswas, Ayan and
O{'}Malley, Daniel and
Most, Alexander and
Wanna, Selma Liliane and
Sreekumar, Ajay",
editor = "Jansen, Peter and
Dalvi Mishra, Bhavana and
Trivedi, Harsh and
Prasad Majumder, Bodhisattwa and
Hope, Tom and
Khot, Tushar and
Downey, Doug and
Horvitz, Eric",
booktitle = "Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities",
month = may,
year = "2025",
address = "Albuquerque, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.aisd-main.6/",
pages = "58--69",
ISBN = "979-8-89176-224-4",
abstract = "Large Language Models (LLMs) are increasinglybeing leveraged for generating andtranslating scientific computer codes by bothdomain-experts and non-domain experts. Fortranhas served as one of the go to programminglanguages in legacy high-performance computing(HPC) for scientific discoveries. Despitegrowing adoption, LLM-based code translationof legacy code-bases has not been thoroughlyassessed or quantified for its usability.Here, we studied the applicability of LLMbasedtranslation of Fortran to C++ as a step towardsbuilding an agentic-workflow using openweightLLMs on two different computationalplatforms. We statistically quantified the compilationaccuracy of the translated C++ codes,measured the similarity of the LLM translatedcode to the human translated C++ code, andstatistically quantified the output similarity ofthe Fortran to C++ translation."
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%0 Conference Proceedings
%T LLM-Assisted Translation of Legacy FORTRAN Codes to C++: A Cross-Platform Study
%A Ranasinghe, Nishath Rajiv
%A Jones, Shawn M.
%A Kucer, Michal
%A Biswas, Ayan
%A O’Malley, Daniel
%A Most, Alexander
%A Wanna, Selma Liliane
%A Sreekumar, Ajay
%Y Jansen, Peter
%Y Dalvi Mishra, Bhavana
%Y Trivedi, Harsh
%Y Prasad Majumder, Bodhisattwa
%Y Hope, Tom
%Y Khot, Tushar
%Y Downey, Doug
%Y Horvitz, Eric
%S Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-224-4
%F ranasinghe-etal-2025-llm
%X Large Language Models (LLMs) are increasinglybeing leveraged for generating andtranslating scientific computer codes by bothdomain-experts and non-domain experts. Fortranhas served as one of the go to programminglanguages in legacy high-performance computing(HPC) for scientific discoveries. Despitegrowing adoption, LLM-based code translationof legacy code-bases has not been thoroughlyassessed or quantified for its usability.Here, we studied the applicability of LLMbasedtranslation of Fortran to C++ as a step towardsbuilding an agentic-workflow using openweightLLMs on two different computationalplatforms. We statistically quantified the compilationaccuracy of the translated C++ codes,measured the similarity of the LLM translatedcode to the human translated C++ code, andstatistically quantified the output similarity ofthe Fortran to C++ translation.
%U https://aclanthology.org/2025.aisd-main.6/
%P 58-69
Markdown (Informal)
[LLM-Assisted Translation of Legacy FORTRAN Codes to C++: A Cross-Platform Study](https://aclanthology.org/2025.aisd-main.6/) (Ranasinghe et al., AISD 2025)
ACL
- Nishath Rajiv Ranasinghe, Shawn M. Jones, Michal Kucer, Ayan Biswas, Daniel O’Malley, Alexander Most, Selma Liliane Wanna, and Ajay Sreekumar. 2025. LLM-Assisted Translation of Legacy FORTRAN Codes to C++: A Cross-Platform Study. In Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities, pages 58–69, Albuquerque, New Mexico, USA. Association for Computational Linguistics.