Fine-tuned LLM-based Code Migration Framework
Oleg Grynets, Vasyl Lyashkevych, Dmytro Baran, Maksym Orliansky, Taras Zelenyy, Markiian Leshchyshyn

TL;DR
This paper introduces a novel framework that uses fine-tuned Large Language Models to automate and improve SQL database migration, achieving higher accuracy and efficiency in code conversion between different database systems.
Contribution
It presents a new LLM-based migration framework that combines fine-tuning, semi-supervised error analysis, and expert feedback for scalable and precise database transformation.
Findings
Significant reduction in syntax error rates.
Improved feature alignment across migration iterations.
Enhanced workflow efficiency through GAI integration.
Abstract
The study presents the outcomes of research and experimental validation in the domain of automated codebase migration, with a focus on addressing challenges in transitioning SQL-based systems. The proposed method for migration essentially appears as a framework that leverages the best aspects of traditional software engineering techniques and provides an iterative, scalable, precise and efficient solution for modern database transformations. The central piece of the approach is the integration of a fine-tuned Large Language Model to address critical issues in SQL code conversion, such as syntax mapping, resolving discrepancies between Oracle PL/SQL and PostgreSQL, and optimising database elements such as stored procedures, triggers, views, and overall database logic. Thus, the method involves a trade-off between fine-tuning and prompt engineering. Special attention is given to a…
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Taxonomy
TopicsSoftware Engineering Research · Advanced Database Systems and Queries · Web Application Security Vulnerabilities
