AnchorRepair: A Reference-Guided Dual-Agent Framework for Code Translation Repair
TL;DR
AnchorRepair is proposed, a dual-agent framework that treats the Java source as first-class diagnostic evidence that generalizes across LLM backends without modification and achieves repair coverage comparable to a general-purpose coding agent at less than half the cost.
Abstract
Automated code translators can now produce repository-scale translations from Java to Python, yet a substantial fraction of translated functions remain semantically incorrect. Repairing such defects differs from traditional automated program repair (APR) because the correctness specification is based on the source language and no target-language test suite exists to guide fault localization. We observe that the original Java code is the most authoritative specification for repair and propose AnchorRepair, a dual-agent framework that treats the Java source as first-class diagnostic evidence. The Diagnosis Agent operates in a ReAct-style loop and autonomously invokes seven cross-lingual analysis tools, producing a structured root-cause diagnosis by contrasting the Java specification against the Python translation across control flow, data flow, and call graph dimensions. A Patch Agent then generates a corrected Python function conditioned on this diagnosis and the retrieved Java context. We evaluated AnchorRepair on 82 real translation defects from four Apache Commons projects. With DeepSeek-V3.2 as the backend, AnchorRepair repairs 40 defects (48.8%), a 12-point absolute improvement over the LLM-Only baseline (28/82) and substantially outperforming TransAgent (17/82) and D4C (21/82). The framework generalizes across LLM backends without modification and achieves repair coverage comparable to a general-purpose coding agent at less than half the cost. Our source code and all experimental results are publicly available at https://github.com/JJS-TJ/AnchorRepair.