LegacyTranslate: LLM-based Multi-Agent Method for Legacy Code Translation

TL;DR

LegacyTranslate is a multi-agent framework utilizing specialized LLMs to improve the accuracy and integration of legacy code translation, specifically from PL/SQL to Java, in enterprise modernization projects.

Contribution

It introduces a novel multi-agent approach with API-aware agents that enhance code translation quality and correctness in real-world legacy system modernization.

Findings

Initial translation achieves 45.6% compilable code

API grounding improves compilation by 8%

Refinement increases test-pass rate by 3%

Abstract

Modernizing large legacy systems remains a major challenge in enterprise environments, particularly when migration must preserve domain-specific logic while conforming to internal architectural frameworks and shared APIs. Direct application of Large Language Models (LLMs) for code translation often produces syntactically valid outputs that fail to compile or integrate within existing production frameworks, limiting their practical adoption in real-world modernization efforts. In this paper, we propose LegacyTranslate, a multi-agent framework for API-aware code translation, developed and evaluated in the context of an ongoing modernization effort at a financial institution migrating approximately 2.5 million lines of PL/SQL to Java. The core idea is to use specialized LLM-based agents, each addressing a different aspect of the translation challenge. Specifically, LegacyTranslate consists of three agents: Initial Translation Agent produces an initial Java translation using retrieved in-context examples; API Grounding Agent aligns the code with existing APIs by retrieving relevant entries from an API knowledge base; and Refinement Agent iteratively refines the output using compiler feedback and API suggestions to improve correctness. Our experiments show that each agent contributes to better translation quality. The Initial Translation Agent alone achieves 45.6% compilable outputs and 30.9% test-pass rate. With API Grounding Agent and Refinement Agent, compilation improves by an additional 8% and test-pass accuracy increases by 3%.