@inproceedings{yang-etal-2025-rust,
    title = "Rust-doctor: Enhanced Feature for Rust Ownership and Lifetime Repair with Balanced Training Data Generation",
    author = "Yang, Wenzhang  and
      Ren, Xiaoning  and
      Gao, Cuifeng  and
      Xue, Yinxing",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.179/",
    doi = "10.18653/v1/2025.findings-emnlp.179",
    pages = "3366--3376",
    ISBN = "979-8-89176-335-7",
    abstract = "As a relatively new programming language, Rust has gained significant popularity in recent years due to its safety features during compilation. However, Rust developers often face challenges stemming from its strict compilation checks due to the steep learning curve of safety rules. To make matters worse, the lack of training data and the unique semantics of Rust lead to poor performance in learning-based automated program repair techniques. To address these challenges, we propose a novel error injection approach to generate a balanced training dataset and leverage the Mid-level Intermediate Representation (MIR) as enhanced features for Rust{'}s unique compilation error repair. Using these innovations, we fine-tuned a new code model, LLaRRA: \textbf{L}arge \textbf{L}anguage \textbf{a}nd \textbf{R}ust \textbf{R}epair \textbf{A}ssistant. Experimental results demonstrate that LLaRRA significantly outperforms state-of-the-art models in terms of Pass@K and Acc@K."
}