AINeutralarXiv – CS AI · 6h ago7/10
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Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
A systematic review of 114 studies reveals that code quality defects in large language models stem primarily from training data imperfections rather than model limitations alone. The research establishes a taxonomy linking 18 propagation mechanisms between data quality issues and generated code failures, while advocating for proactive data governance over reactive post-generation filtering.