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

2026-05-17

AI Summary

This work addresses the lack of standardized governance in AI-assisted development of scientific software, which currently hinders compliance with stringent quality assurance requirements such as ASME NQA-1. The authors propose a structured framework that, for the first time, integrates large language models with software verification and validation (V&V) methodologies under NQA-1 compliance. Using the open-source nuclear-grade code TMAP8 as a testbed, they establish an AI-assisted V&V use case development process that is verifiable, traceable, and auditable. The framework explicitly defines mechanisms for disclosing AI-generated content, subjecting it to rigorous review, and assigning human accountability, thereby unifying transparency, reproducibility, and regulatory compliance. This approach ensures software correctness while meeting the rigorous demands of high-assurance quality standards.

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.

Problem

• AI-assisted development
• scientific software
• Software Quality Assurance
• traceability
• NQA-1

Innovation

• AI-assisted development
• traceability
• verification and validation (V&V)
• NQA-1
• scientific software governance