Software repositories is one of the sources of data in Empirical Software Engineering, primarily in the Mining Software Repositories field, aimed at extracting knowledge from the dynamics and practice of software projects. With the emergence of social coding platforms such as GitHub, researchers have now access to millions of software repositories to use as source data for their studies. With this massive amount of data, sampling techniques are needed to create more manageable datasets. The creation of these datasets is a crucial step, and researchers have to carefully select the repositories to create representative samples according to a set of variables of interest. However, current sampling methods are often based on random selection or rely on variables which may not be related to the research study (e.g., popularity or activity). In this paper, we present a methodology for creating representative samples of software repositories, where such representativeness is properly aligned with both the characteristics of the population of repositories and the requirements of the empirical study. We illustrate our approach with use cases based on Hugging Face repositories.

翻译：软件仓库是实证软件工程领域的数据来源之一，主要应用于软件仓库挖掘方向，旨在从软件项目的动态特征与实践模式中提取知识。随着GitHub等社交编程平台的出现，研究者如今能够访问数以百万计的软件仓库作为研究数据源。面对如此海量的数据，需要采用抽样技术来构建更易处理的数据集。数据集的构建是关键步骤，研究者必须根据一组目标变量仔细选择仓库以创建代表性样本。然而，当前的抽样方法通常基于随机选择，或依赖于可能与研究目标无关的变量（例如流行度或活跃度）。本文提出一种构建软件仓库代表性样本的方法论，该方法能确保样本代表性既符合仓库总体的特征，又满足实证研究的要求。我们通过基于Hugging Face仓库的用例来阐释该方法。