Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. Aims: We apply an SE-oriented classification to PTMs and datasets on a popular open-source ML repository, Hugging Face (HF), and analyze the evolution of PTMs over time. Method: We conducted a repository mining study. We started with a systematically gathered database of PTMs and datasets from the HF API. Our selection was refined by analyzing model and dataset cards and metadata, such as tags, and confirming SE relevance using Gemini 1.5 Pro. All analyses are replicable, with a publicly accessible replication package. Results: The most common SE task among PTMs and datasets is code generation, with a primary focus on software development and limited attention to software management. Popular PTMs and datasets mainly target software development. Among ML tasks, text generation is the most common in SE PTMs and datasets. There has been a marked increase in PTMs for SE since 2023 Q2. Conclusions: This study underscores the need for broader task coverage to enhance the integration of ML within SE practices.

本文解决了开放源代码预训练模型和数据集在软件工程中的分类不足的问题，提出了针对软件工程需求的分类方法。研究发现，目前大多数模型和数据集聚焦于代码生成和软件开发，强调了在软件管理任务中关注的不足，并指出自2023年第二季度以来，针对软件工程的预训练模型数量显著增加。这表明亟需扩展任务覆盖面，以提升机器学习在软件工程中的应用整合。

面向软件工程的开放源代码机器学习模型和数据集分类研究