Migrating Esope to Fortran 2008 using model transformations
- URL: http://arxiv.org/abs/2601.21755v1
- Date: Thu, 29 Jan 2026 14:11:18 GMT
- Title: Migrating Esope to Fortran 2008 using model transformations
- Abstract summary: We present an approach for automatically migrating FORTRAN 77 with a proprietary extension, named Esope, to Fortran 2008.<n>While supporting readability of the generated code, we want to maintain the level of abstraction provided by Esope.<n>Our method uses model-driven engineering techniques, with transformations to generate a target model from which we export easy-to-read Fortran 2008 source code.
- Score: 0.1141780544549208
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Legacy programming languages such as FORTRAN 77 still play a vital role in many industrial applications. Maintaining and modernizing these languages is challenging, especially when migrating to newer standards such as Fortran 2008. This is exacerbated in the presence of legacy proprietary extensions on such legacy languages, because their semantics are often based on old context (limits of legacy language, domain logic,...). This paper presents an approach for automatically migrating FORTRAN 77 with a proprietary extension, named Esope, to Fortran 2008. We introduce a tool that converts Esope source code to Fortran 2008. While supporting readability of the generated code, we want to maintain the level of abstraction provided by Esope. Our method uses model-driven engineering techniques, with transformations to generate a target model from which we export easy-to-read Fortran 2008 source code. We discuss the advantages, limitations, and maintainability considerations of our approach and provide insights into its scalability and adaptability to evolving requirements.
Related papers
- A Fortran General-Purpose Transpiler: Proof of Concept [0.0]
We present FGPT, a transpiler framework designed to bridge this gap.<n>It provides a systematic pipeline that transpiles Fortran into GPU-adapted Fortran.<n>It produces correct, differentiable Python implementations without requiring manual intervention.
arXiv Detail & Related papers (2026-07-31T13:38:16Z) - SEDCoT: Enhancing LLM-Based COBOL Code Translation via Symbolic Execution and Delta Debugging [49.898541267264115]
Maintenance is increasingly challenging due to outdated technologies, sparse documentation, and developer retirement.<n>Traditional rule-based transcompilers yield outputs that are difficult to read and maintain.<n>We propose SEDCoT, a novel-to-C translation framework.
arXiv Detail & Related papers (2026-07-05T03:07:10Z) - OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms [13.343925256921722]
We introduce OMPILOT, a domain-specific encoder-decoder transformer tailored for translating C++ code into OpenMP.<n>OMPBLEU is a novel composite metric crafted to assess the correctness and quality of OpenMP parallel constructs.
arXiv Detail & Related papers (2025-11-05T21:21:15Z) - From Legacy Fortran to Portable Kokkos: An Autonomous Agentic AI Workflow [0.11862655008303463]
Large language models (LLMs) have shown promise in source-to-source code generation.<n>This paper presents an agentic AI workflow where specialized "agents" collaborate to translate, validate, compile, run, test, debug, and optimize Fortran kernels into portable Kokkos C++ programs.<n>Results show the pipeline modernizes a range of benchmark kernels, producing performance-portable Kokkos codes across hardware partitions.
arXiv Detail & Related papers (2025-09-15T20:50:15Z) - Guided Tensor Lifting [54.10411390218929]
Domain-specific languages (s) for machine learning are revolutionizing the speed and efficiency of machine learning workloads.<n>To take advantage of these capabilities, a user must first translate their legacy code from the language it is currently written in, into the new DSL.<n>Process of automatically lifting code into these DSLs has been identified by several recent works, which propose program synthesis as a solution.
arXiv Detail & Related papers (2025-04-28T12:00:10Z) - Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing [0.9668407688201359]
generative artificial intelligence (GenAI) is poised to transform productivity in scientific computing.<n>We developed a tool, CodeScribe, which combines prompt engineering with user supervision to establish an efficient process for code conversion.<n>We also address the challenges of AI-driven code translation and highlight its benefits for enhancing productivity in scientific computing.
arXiv Detail & Related papers (2024-10-31T16:48:41Z) - CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to better interpret the programming domain knowledge.<n>CodeGRAG significantly improves the code generation ability of LLMs and can even offer performance gain for cross-lingual code generation.
arXiv Detail & Related papers (2024-05-03T02:48:55Z) - CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models.
CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
arXiv Detail & Related papers (2024-04-03T02:21:46Z) - mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR
using Program Synthesis [48.01697184432969]
mlirSynth translates programs from lower-level MLIR dialects to high-level ones without manually defined rules.
We demonstrate its effectiveness reviby raising C programs to two distinct high-level MLIR dialects, which enables us to use existing high-level dialect specific compilation flows.
arXiv Detail & Related papers (2023-10-06T12:21:50Z) - Parsing Fortran-77 with proprietary extensions [0.0]
Many organizations still rely on old code written in "obsolete" programming languages.
One difficulty of working with these "veteran languages" is being able to parse the source code to build a representation of it.
arXiv Detail & Related papers (2023-09-05T07:54:02Z) - Automated Code generation for Information Technology Tasks in YAML
through Large Language Models [56.25231445614503]
We present Wisdom, a natural-language to-YAML code generation tool, aimed at improving IT automation productivity.
We develop two novel performance metrics for YAML and to capture the specific characteristics of this domain.
arXiv Detail & Related papers (2023-05-02T21:01:01Z) - Augmented Language Models: a Survey [55.965967655575454]
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools.
We refer to them as Augmented Language Models (ALMs)
The missing token objective allows ALMs to learn to reason, use tools, and even act, while still performing standard natural language tasks.
arXiv Detail & Related papers (2023-02-15T18:25:52Z) - Summarize and Generate to Back-translate: Unsupervised Translation of
Programming Languages [86.08359401867577]
Back-translation is widely known for its effectiveness for neural machine translation when little to no parallel data is available.
We propose performing back-translation via code summarization and generation.
We show that our proposed approach performs competitively with state-of-the-art methods.
arXiv Detail & Related papers (2022-05-23T08:20:41Z) - Toward Modern Fortran Tooling and a Thriving Developer Community [0.0]
Fortran is the oldest high-level programming language that remains in use today.
It is one of the dominant languages used for compute-intensive scientific and engineering applications.
In this paper we report on the progress to date and outline the next steps.
arXiv Detail & Related papers (2021-09-15T15:43:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.