Paper ID: 2412.19770

Fortran2CPP: Automating Fortran-to-C++ Migration using LLMs via Multi-Turn Dialogue and Dual-Agent Integration

Le Chen, Bin Lei, Dunzhi Zhou, Pei-Hung Lin, Chunhua Liao, Caiwen Ding, Ali Jannesari

Migrating Fortran code to C++ is a common task for many scientific computing teams, driven by the need to leverage modern programming paradigms, enhance cross-platform compatibility, and improve maintainability. Automating this translation process using large language models (LLMs) has shown promise, but the lack of high-quality, specialized datasets has hindered their effectiveness. In this paper, we address this challenge by introducing a novel multi-turn dialogue dataset, Fortran2CPP, specifically designed for Fortran-to-C++ code migration. Our dataset, significantly larger than existing alternatives, is generated using a unique LLM-driven, dual-agent pipeline incorporating iterative compilation, execution, and code repair to ensure high quality and functional correctness. To demonstrate the effectiveness of our dataset, we fine-tuned several open-weight LLMs on Fortran2CPP and evaluated their performance on two independent benchmarks. Fine-tuning on our dataset led to remarkable gains, with models achieving up to a 3.31x increase in CodeBLEU score and a 92\% improvement in compilation success rate. This highlights the dataset's ability to enhance both the syntactic accuracy and compilability of the translated C++ code. Our dataset and model have been open-sourced and are available on our public GitHub repository\footnote{\url{this https URL}}.

Submitted: Dec 27, 2024