梳理Hugging Face上开源模型与数据集，发现代码生成是主流，但软件管理关注不足。
Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
- 基于SE需求对Hugging Face资源分类，筛选出相关模型与数据集
- 2023年第二季度后，软件工程类模型数量显著增长，代码生成为主流任务
- 适合关注AI赋能软件开发的工程师和研究者参考
背景：开源预训练模型（PTMs）和数据集为各类机器学习任务提供了丰富资源，但现有资源缺乏面向软件工程（SE）需求的分类体系。目标：针对主流开源机器学习平台Hugging Face（HF），应用以软件工程为导向的分类方法，分析模型与数据集的演变趋势。方法：开展仓库挖掘研究，通过HF API获取系统化数据库，结合模型卡、数据集卡及元数据（如标签）进行筛选，并利用Gemini 1.5 Pro验证其与软件工程的相关性。所有分析均具备可复现性，附有公开可访问的复现包。结果：在开源模型与数据集中，代码生成是最常见的软件工程任务，主要聚焦于软件开发，对软件管理的关注较少；主流模型与数据集多服务于软件开发场景。在机器学习任务中，文本生成是软件工程类模型与数据集中最普遍的任务。自2023年第二季度以来，软件工程类预训练模型数量明显上升。结论：当前资源仍需拓展任务覆盖范围，以促进机器学习在软件工程实践中的更广泛应用。
原文摘要 · Abstract (English)
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.
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