Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement

Understand

Recent advances in leveraging LLMs for APR have demonstrated impressive capabilities in fixing software defects.

• However, current LLM-based approaches predominantly focus on mainstream programming languages like Java and Python, neglecting less prevalent but emerging languages such as Rust due to expensive training resources, limited datasets, and insufficient community support.
• This narrow focus creates a significant gap in repair capabilities across the programming language spectrum, where the full potential of LLMs for comprehensive multilingual program repair remains largely unexplored.
• To address this limitation, we introduce a novel cross-language program repair approach LANTERN that leverages LLMs' differential proficiency across languages through a multi-agent iterative repair paradigm.