RepairChemist: Cross-Language Repair Skill Distillation for Low-Resource Program Repair

Large language models (LLMs) have made substantial progress in program repair for high-resource programming languages (PLs) such as Java, yet a clear performance gap remains for languages with comparatively scarce repair data, such as Rust. Existing approaches use fine-tuning or program translation to alleviate this gap, but may introduce additional adaptation costs or semantic inconsistencies. In this work, we propose RepairChemist, a cross-language repair knowledge transfer framework that abstracts repair knowledge from high-resource PLs into reusable repair skills and guides program repair in low-resource PLs. Specifically, RepairChemist distills defect patterns and repair actions from bug-fix pairs in high-resource PLs, and organizes the resulting skills into skill groups based on statement types, forming a structured skill library. During low-resource program repair, RepairChemist retrieves applicable skills for the current defect based on its statement types and defect patterns to guide patch generation. Experiments on xCodeEval and SWE-bench Multilingual show that RepairChemist consistently improves repair performance across low-resource PLs and LLMs in both procedural and agentic repair settings, without parameter updates or program translation.