2025/05/16 by Maria Dziuba, Dziuba, Maria, Valentin Malykh +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Software Engineering (cs.SE) #Software Engineering Research #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2505.11026
openalex publication_date 2025/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Structured code comments in docstring format are essential for code comprehension and maintenance, but existing machine learning models for their generation perform poorly for Russian compared to English. To bridge this gap, we present StRuCom - the first large-scale dataset (153K examples) specifically designed for Russian code documentation. Unlike machine-translated English datasets that distort terminology (e.g., technical loanwords vs. literal translations) and docstring structures, StRuCom combines human-written comments from Russian GitHub repositories with synthetically generated ones, ensuring compliance with Python, Java, JavaScript, C#, and Go standards through automated validation. Fine-tuning Qwen2.5-Coder models (0.5B-7B) on StRuCom shows statistically significant improvements of chrf++ and BERTScore over baseline models.