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Conference Paper

Eliminating QC Programming Duplication Through Claude AI-Assisted Independent Code Generation: A Practical Framework for Regulatory-Compliant Validation

Jaime Yan, Jason Zhang

PharmaSUG 2026 · 2026 · AI-201

DOI · 10.5281/zenodo.22182922 Companion code

Abstract

Independent QC programming — recreating production programs from specifications alone — accounts for an estimated 30–50% of total clinical programming effort. This paper presents a Claude AI-assisted workflow that generates independent QC code in Python directly from ADaM specifications, combining a QC Trace Tree, a Decision Router, Agent Skills, and an automated code review engine to reduce duplication between production and QC streams while preserving operational independence through isolated AI instances, with the human QC programmer as the genuinely independent review layer. On the CDISCPilot01 benchmark, the framework achieved 97.1%–100% variable-level match across five ADaM domains and passed all 13 assertions. Companion code is publicly available.

Keywords

QC programming · Claude · ADaM · CDISC · AI agents · automated code review

Cite

Jaime Yan, Jason Zhang. "Eliminating QC Programming Duplication Through Claude AI-Assisted Independent Code Generation: A Practical Framework for Regulatory-Compliant Validation." PharmaSUG 2026, 2026. doi:10.5281/zenodo.22182922.