AI 中文总结
该研究提出结合RM-ODP、LLMs和DSM分析的AI辅助迁移框架，成功将两个不同领域的遗留科学应用转换为保留原功能的可重用云工作流，为遗留科学软件现代化提供可行方案。
AI 中文摘要
遗留科学应用仍是有价值的研究资产，但通常与项目特定的执行环境紧密耦合，限制了它们在现代科学工作流系统和云原生虚拟研究环境（VREs）中的复用性、可复现性和部署能力。现有迁移方法主要针对单个制品，如笔记本或容器，对将异构遗留应用系统转换为可重用云原生工作流的支持有限。本文提出一种AI辅助迁移框架，结合开放分布式处理参考模型（RM-ODP）引导的架构分析、大语言模型（LLMs）和设计结构矩阵（DSM）分析。该框架首先利用RM-ODP引导LLM从异构遗留应用中识别可重用工作流组件、其接口和执行依赖关系，随后通过DSM分析迭代评估和优化生成的工作流结构，最后基于LLM的工作流生成器实现经验证的工作流组件，并生成容器化执行环境和可执行工作流定义，用于在包括VREs在内的云原生工作流系统中部署。该框架在两个来自不同科学领域的遗留科学应用上进行了评估，两种情况下应用均成功转换为可重用云工作流，同时保留了原始功能，证明了所提方法在遗留科学软件现代化方面的可行性。
英文摘要
Legacy scientific applications remain valuable research assets but are often tightly coupled to project-specific execution environments, limiting their reuse, reproducibility, and deployment within modern scientific workflow systems and cloud-native Virtual Research Environments (VREs). Existing migration approaches primarily target individual artifacts, such as notebooks or containers, and provide limited support for systematically transforming heterogeneous legacy applications into reusable cloud-native workflows. This paper presents an AI-assisted migration framework that combines the Reference Model of Open Distributed Processing (RM-ODP)-guided architectural analysis, Large Language Models (LLMs), and Design Structure Matrix (DSM) analysis. The framework first uses RM-ODP to guide an LLM in identifying reusable workflow components, their interfaces, and execution dependencies from heterogeneous legacy applications. The resulting workflow structure is then iteratively evaluated and refined using DSM analysis. Finally, an LLM-based workflow generator implements the validated workflow components and produces containerized execution environments and executable workflow definitions for deployment in cloud-native workflow systems, including VREs. The framework was evaluated on two legacy scientific applications from different scientific domains. In both cases, the applications were successfully transformed into reusable cloud-native workflows while preserving their original functionality, demonstrating the feasibility of the proposed approach for modernizing legacy scientific software.
Comments8 pages, 2 figures, 5 tables. Accepted at the 22nd IEEE International Conference on e-Science (eScience 2026)
