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LLM-Assisted Translation of Legacy FORTRAN Codes to C++: A Cross-Platform Study

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arxiv 2504.15424 v1 pith:L4RQZNA6 submitted 2025-04-21 cs.SE cs.AI

Nishath Rajiv Ranasinghe , Shawn M. Jones , Michal Kucer , Ayan Biswas , Daniel O'Malley , Alexander Buschmann Most , Selma Liliane Wanna , Ajay Sreekumar This is my paper · ORCID
classification cs.SEcs.AI
keywords fortrantranslationcodecodeslegacyquantifiedtranslatedllm-based
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) are increasingly being leveraged for generating and translating scientific computer codes by both domain-experts and non-domain experts. Fortran has served as one of the go to programming languages in legacy high-performance computing (HPC) for scientific discoveries. Despite growing adoption, LLM-based code translation of legacy code-bases has not been thoroughly assessed or quantified for its usability. Here, we studied the applicability of LLM-based translation of Fortran to C++ as a step towards building an agentic-workflow using open-weight LLMs on two different computational platforms. We statistically quantified the compilation accuracy of the translated C++ codes, measured the similarity of the LLM translated code to the human translated C++ code, and statistically quantified the output similarity of the Fortran to C++ translation.

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  1. SEDCoT: Enhancing LLM-Based COBOL Code Translation via Symbolic Execution and Delta Debugging

    cs.SE 2026-07 conditional novelty 6.0 of 10

    SEDCoT combines LLM translation, symbolic-execution test generation, and delta-debugging repair to raise COBOL-to-C correctness by ≥12% over SOTA LLM baselines while preserving human-readable output.

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