An AI-Assisted Migration Framework for Transforming Legacy Scientific Applications into Reusable Cloud-Based Workflows
2026-08-24 • Software Engineering
Software Engineering
AI summaryⓘ
The authors created a new method to modernize old scientific software so it can be reused more easily in today's cloud computing environments. They used a combination of architectural analysis, AI language models, and a technique to understand dependencies between parts of the software. Their method identifies key components, refines the workflow, and then generates cloud-friendly versions that still work the same as before. They tested this approach on two different scientific applications and successfully converted them into modern, reusable workflows.
legacy scientific applicationscloud-native workflowsReference Model of Open Distributed Processing (RM-ODP)Large Language Models (LLMs)Design Structure Matrix (DSM) analysisworkflow componentscontainerizationcloud computingVirtual Research Environments (VREs)software modernization
Authors
Nafiseh Soveizi, Sven Tesselaar, Hero Robinson Brouwer, Zhiming Zhao
Abstract
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.