Multi-Agent System Boosts Code Translation Accuracy to 94%

Translating software code from one programming language to another has long challenged developers managing evolving systems and code migration efforts. While Large Language Models have opened new possibilities for automating this task, current approaches struggle to maintain accuracy and don't fully exploit the contextual and structural information embedded in source code.

The BabelCoder Approach#

BabelCoder introduces an agentic framework that transforms code translation from a single-step problem into a coordinated, multi-stage workflow. Rather than attempting translation in isolation, the system deploys specialized agents that each handle a distinct responsibility: one generates the initial translation, another validates the output against test cases, and additional agents identify and repair any errors that emerge. This division of labor allows each component to focus deeply on its specific task.

Performance Gains#

Testing across four established benchmark datasets, BabelCoder demonstrates substantial improvements over four leading competing methods. The framework achieves an average accuracy of 94.16%, outperforming existing solutions by margins ranging from 0.5% to 13.5% in 94% of the tested cases. These results suggest that the collaborative agent approach more effectively preserves code functionality across language boundaries than previous translation methods.

Why It Matters#

As development teams increasingly work across multiple languages and face legacy system modernization, reliable code translation tools become more valuable. By combining generative capability with systematic validation and repair cycles, BabelCoder moves automated translation closer to practical deployment in real-world software engineering workflows.

For details, see the full paper on arXiv.