# Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing

arXiv: https://arxiv.org/abs/2410.24119 · HTML: https://mlsanity.com/paper/2410.24119/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)

## Built on by (most cited first, 9 of 9)

- [AInsteinBench: Benchmarking Coding Agents on Scientific Repositories](https://mlsanity.com/paper/2512.21373/) (2025)
- [SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion](https://mlsanity.com/paper/2510.17925/) (2025)
- [Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation](https://mlsanity.com/paper/2512.03086/) (2025)
- [Systematic LLM Translation of Legacy Scientific Code to Differentiable Frameworks: Application to a Land Surface Model](https://mlsanity.com/paper/2606.07681/) (2026)
- [Adding New Capability in Existing Scientific Application with LLM Assistance](https://mlsanity.com/paper/2511.00087/) (2025)
- [Can Large Language Models Implement Agent-Based Models? An ODD-based Replication Study](https://mlsanity.com/paper/2602.10140/) (2026)
- [Beyond BLEU: A Semantic Evaluation Method for Code Translation](https://mlsanity.com/paper/2605.05282/) (2026)
- [Exploring the Role of LLMs in HPC Programming: A Survey](https://mlsanity.com/paper/2608.26110/) (2026)
- [CURATE: Leveraging LLM Agents to Compose, Catalog, and Deploy Reproducible Workflows](https://mlsanity.com/paper/2608.04270/) (2026)

## Abstract (arXiv)

The emergence of foundational models and generative artificial intelligence (GenAI) is poised to transform productivity in scientific computing, especially in code development, refactoring, and translating from one programming language to another. However, because the output of GenAI cannot be guaranteed to be correct, manual intervention remains necessary. Some of this intervention can be automated through task-specific tools, alongside additional methodologies for correctness verification and effective prompt development. We explored the application of GenAI in assisting with code translation, language interoperability, and codebase inspection within a legacy Fortran codebase used to simulate particle interactions at the Large Hadron Collider (LHC). In the process, we developed a tool, CodeScribe, which combines prompt engineering with user supervision to establish an efficient process for code conversion. In this paper, we demonstrate how CodeScribe assists in converting Fortran code to C++, generating Fortran-C APIs for integrating legacy systems with modern C++ libraries, and providing developer support for code organization and algorithm implementation. We also address the challenges of AI-driven code translation and highlight its benefits for enhancing productivity in scientific computing workflows.