N. Soveizi

An AI-Assisted Migration Framework for Transforming Legacy Scientific Applications into Reusable Cloud-Based Workflows

An AI-assisted migration framework that combines the Reference Model of Open Distributed Processing-guided architectural analysis, Large Language Models (LLMs), and Design Structure Matrix (DSM) analysis is presented, demonstrating the feasibility of the proposed approach for modernizing legacy scientific software.

UvA-DARE (Digital Academic Repository) Socially-Informed Jupyter Notebook Quality

A Multi-Viewpoint Modeling Framework for Digital Twin Integration and Reuse with LLM-Assisted Compatibility Analysis

Digital Twin (DT) ecosystems integrate heterogeneous computational models to represent complex systems under evolving, purpose-specific objectives. Systematic reuse of existing high-quality models and datasets is essential for scalable DT development, yet is constrained by heterogeneity in semantic intent, data structu...

LLMCrater: Lifecycle-Aware FAIR Metadata Generation using Large Language Models

LLMCrater is a lifecycle-aware metadata generation framework that combines Large Language Models (LLMs) with stage-specific RO-Crate metadata profiles that progressively enriches metadata across four research lifecycle stages while remaining compatible with RO-Crate~1.1 and EOSC metadata recommendations.

From Metrics to Improvement: A Lifecycle-Aware LLM Feedback Framework for Research Software Quality

A lifecycle-aware framework that integrates quantitative software quality assessment with Large Language Model (LLM)-based code refinement is proposed and the potential of metric-driven LLM feedback for research software quality improvement is demonstrated while highlighting its inherently multi-objective nature.