An in-depth study of the effects of methods on the dataset selection of public development projects
An in-depth study of the effects of methods on the dataset selection of public development projects
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DOI：
10.1049/sfw2.12050
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Can Cheng;Bing Li;Zengyang Li;Peng Liang;Xu Yang
中科院分区：
文献类型：
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作者：
Can Cheng;Bing Li;Zengyang Li;Peng Liang;Xu Yang
公共开发项目（PDP）和有文件证明的公共开发项目（DPDP）是两种类型的项目，可提供关于开发人员和用户如何参与开放源码软件项目的宝贵信息。然而，由于缺乏具体的项目选择方法，研究人员很难有效地选择PDP和DPDP这两种类型的项目。为了解决这个问题，对标准数据集进行了标记，并测试了60种配置下的基线方法（即根据星星数等单个特征选择项目）和18种配置下的机器学习方法，以确定用于选择PDP和DPDP的精度和F-测量的最佳配置。结果表明：（1）对于选择精度较高的PDP或DPDP，基线法的精度最高，分别为0.877（PDP）和0.831（DPDP）;（2）对于选择F-测度较高的PDP或DPDP，机器学习法的F-测度最高，分别为0.817（PDP）和0.789（DPDP）;（3）将已有的样本选择策略与机器学习方法相结合，可以使PDP的选择精度提高6.39%~ 41.33%，DPDP的选择精度提高35.50%~ 269.02%。
Public development projects (PDPs) and documented public development projects (DPDPs) are two types of projects that can provide valuable information on how developers and users participate in OSS projects. However, it is hard for researchers to effectively select PDPs and DPDPs due to the lack of specific project selection methods for these two types of projects. To address this problem, a standard dataset was labelled and the base line methods (i.e. selecting projects according to a single feature like star number) under 60 configurations and the machine learning methods under 18 configurations were tested to identify the best configurations in precision and F‐measure for selecting PDPs and DPDPs. The results show that (1) to select PDPs or DPDPs with a high precision, the base line method is the best with precision of 0.877 (PDPs) and 0.831 (DPDPs); (2) to select PDPs or DPDPs with a high F‐measure, the machine learning methods are the best, with F‐measure of 0.817 (PDPs) and 0.789 (DPDPs); (3) existing sample selection strategies can be combined with the machine learning methods, and the precision of selecting PDPs can be increased by 6.39%–41.33% and the precision of selecting DPDPs can be can be increased by 35.50%–269.02%.