CodeR3: A GenAI-Powered Workflow Repair and Revival Ecosystem

TL;DR

CodeR3 leverages generative AI to automate the revival of decayed scientific workflows, transforming legacy systems into modern formats while incorporating visualization, automated service substitution, and human validation to ensure correctness.

Contribution

This paper introduces CodeR3, a novel AI-powered system for migrating and reviving legacy scientific workflows with integrated visualization and human-in-the-loop validation.

Findings

Automation reduces manual effort in workflow parsing.

Service substitution and data validation still need human expertise.

Feasibility demonstrated through Taverna workflow revival case studies.

Abstract

Scientific workflows encode valuable domain expertise and computational methodologies. Yet studies consistently show that a significant proportion of published workflows suffer from decay over time. This problem is particularly acute for legacy workflow systems like Taverna, where discontinued services, obsolete dependencies, and system retirement render previously functional workflows unusable. We present a novel legacy workflow migration system, called CodeR$^3$ (stands for Code Repair, Revival and Reuse), that leverages generative AI to analyze the characteristics of decayed workflows, reproduce them into modern workflow technologies like Snakemake and VisFlow. Our system additionally integrates stepwise workflow analysis visualization, automated service substitution, and human-in-the-loop validation. Through several case studies of Taverna workflow revival, we demonstrate the feasibility of this approach while identifying key challenges that require human oversight. Our findings reveal that automation significantly reduces manual effort in workflow parsing and service identification. However, critical tasks such as service substitution and data validation still require domain expertise. Our result will be a crowdsourcing platform that enables the community to collaboratively revive decayed workflows and validate the functionality and correctness of revived workflows. This work contributes a framework for workflow revival that balances automation efficiency with necessary human judgment.