@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://preview.aclanthology.org/fix-sig-urls/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."
}