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Horizon: Robust Checks for SQL Migration Using LLMs
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AI
DOI：10.14778/3750601.3750646.png)
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Abstract
En 中文
Large language models (LLMs) have recently demonstrated strong capabilities in code migration across languages, making them promising for SQL schema migration. However, achieving reliable and accurate SQL migration with LLMs remains a challenge. This paper presents the first comprehensive approach for practical and effective SQL schema migration using LLMs. We highlight the necessity of robust evaluation and iterative query refinement to achieve highly accurate migrations. Building on traditional database tools along with LLMs, we introduce novel checks to guide LLMs towards syntactically complete and functionally equivalent translations. Our approach supports all schema object types, including complex procedural constructs. Our demonstrations offer audience opportunities to explore our system using a variety of configurations, datasets and custom inputs, providing useful insights into the underlying techniques, their strengths, and limitations.