Hybrid system improves Oracle to PostgreSQL database migration accuracy
A Hybrid Dependency-Aware Framework for Task Decomposition and Dynamic Agent Generation in Oracle-to-PostgreSQL Migration
Logic in Computer ScienceArtificial IntelligenceMultiagent SystemsSoftware Engineering
Summary
Migrating complex Oracle databases to PostgreSQL is difficult because many different code pieces depend on each other. The authors propose a system that carefully figures out these dependencies to break down tasks and create specialized helpers to handle each part. They use a mix of traditional parsing and AI fallback to cover tricky cases. Their system successfully regenerated many database components while respecting the correct order and dependencies, helping avoid errors. This work shows smarter ways to organize and validate database migration tasks.
What this means in practice
- •For database migration engineers: Automate splitting complex Oracle databases into well-ordered tasks to improve PostgreSQL migration accuracy and reduce errors.
- •For enterprise software teams: Generate runtime migration agents tailored to different database code components, improving maintenance and execution feedback during large Oracle to PostgreSQL transitions.
Authors
Oleg Grynets, Oleg Kaskun, Alona Seletska, Daryna Tukalo, Vasyl Lyashkevych
Abstract
Large language model (LLM)-based database migration is often treated as direct code transformation, although enterprise Oracle systems contain heterogeneous SQL and PL/SQL artifacts with different dependencies, execution order, complexity, and validation needs. This paper proposes a hybrid dependency-aware framework that identifies migration tasks, builds a cross-file dependency graph, condenses cyclic dependencies, and uses task specifications to generate specialized migration agents at runtime. The deterministic path combines ANTLR-based parsing with typed dependency extraction, while an LLM fallback is invoked only for units that cannot be parsed reliably. On a corpus of 116 Oracle files, the pipeline produced 1,037 units with zero coverage gaps and 1,271 AST-derived dependencies. The fallback processed 165 parse-error units, recovered 496 additional validated dependencies, eliminated unresolved-dependency units, and increased resolved internal edges from 446 to 527. The graph contained four dependency-respecting phases, while cycle handling was validated separately using Tarjan SCC condensation. A complementary experiment on 1,006 PL/SQL files regenerated 623 scripts (~62%), of which 380 (~61%) executed successfully in PostgreSQL 16. Tables achieved about 85% regeneration success, whereas no query regenerations succeeded under the evaluated specification-mediated baseline, and procedural objects remained strongly dependent on schema context. These results motivate dependency-aware context delivery, task-specific validation, and differentiated agent routing. The paper also formalizes task-to-agent mapping, introduces monitoring and diagnostics for execution feedback, and defines a controlled comparison of monolithic, static-decomposition, dependency-aware, and dynamically orchestrated migration strategies.