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 fine-tuning approach, which enhances the adaptability and compatibility with migration requirements across the entire database. According to the achieved results, fine-tuning plays a very important role. The study employs targeted evaluation methodologies along with computational metrics to measure the success of iterative conversion cycles. Core innovations include automated SQL feature detection, semi-supervised error analysis and integration of Subject Matter Experts feedback within a systematic migration workflow. The methodology achieves significant reductions in Syntax Error Rates, enhances feature alignment throughout migration iterations, and leverages dataset sampling to ensure continual improvement. By embedding GAI into the migration process, the framework facilitates precise feature mapping, semi-automated error resolution, and data-driven optimisation loops, improving workflow efficiency.

翻译：本研究展示了在自动化代码库迁移领域的研究成果与实验验证，重点解决基于SQL系统的迁移挑战。所提出的迁移方法本质上呈现为一个框架，该框架融合了传统软件工程技术的优势，并为现代数据库转换提供了迭代式、可扩展、精确且高效的解决方案。该方法的核心在于集成一个经过微调的大语言模型，以解决SQL代码转换中的关键问题，例如语法映射、解决Oracle PL/SQL与PostgreSQL之间的差异，以及优化存储过程、触发器、视图和整体数据库逻辑等数据库元素。因此，该方法涉及微调与提示工程之间的权衡。研究特别关注微调方法，该方法增强了整个数据库迁移需求的适应性和兼容性。根据所获结果，微调发挥着至关重要的作用。本研究采用针对性评估方法及计算指标来衡量迭代转换周期的成功率。核心创新包括自动化SQL特征检测、半监督错误分析以及在系统化迁移工作流中集成领域专家反馈。该方法显著降低了语法错误率，提升了迁移迭代过程中的特征对齐度，并利用数据集采样确保持续改进。通过将生成式人工智能嵌入迁移流程，该框架实现了精确的特征映射、半自动化错误解决和数据驱动的优化循环，从而提升了工作流效率。