Bridging the Gap on AI-Assisted Scientific Software Development Through Transparency and Traceability

TL;DR

This paper addresses the need for responsible governance of AI-assisted scientific software development, emphasizing transparency, traceability, and verification within strict quality assurance standards using a practical framework.

Contribution

It introduces a structured framework for AI-assisted verification and validation in scientific software, demonstrated through a case study on a fusion energy code.

Findings

Framework aligns with NQA-1 standards

Ensures traceability and accountability in AI-assisted development

Provides practical guidance for V&V case development

Abstract

The widespread adoption of AI-assisted development in scientific software is not a future concern -- it is a present reality. Researchers are already using large language models to write code, generate test cases, and draft documentation, yet this practice remains largely unacknowledged and unguided in formal workflows and published work. This ad hoc, ungoverned use of AI represents a systemic risk to scientific software quality, particularly in safety-relevant modeling and simulation tools subject to strict Software Quality Assurance (SQA), or even Nuclear Quality Assurance Level 1 (NQA-1) standards, for which traceability, independent verification, and documented procedures are paramount. The question facing the scientific software community is, therefore, not whether to permit AI-assisted development, but how to govern it responsibly. This paper proposes guidance for AI-assisted code development in the context of strict software quality assurance. Using TMAP8 -- an open-source tritium migration code for fusion energy -- as a demonstration platform, we propose a structured framework for AI-assisted verification and validation (V&V) case development. V&V case development represents the ideal proving ground for establishing that governance: because validation cases have known solutions, correctness is objectively measurable, errors are identifiable by design, and the artifacts are fully auditable. The proposed guidance, developed based on practical experience described herein, operates within NQA-1 requirements, preserves human accountability, and establishes the disclosure and review standards that responsible AI-assisted scientific software development demands.