Modeling the impact of Python and R packages using dependency and contributor networks
Modeling the impact of Python and R packages using dependency and contributor networks
复制标题
使用依赖关系和贡献者网络对 Python 和 R 包的影响进行建模
DOI：
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发表时间：
2019
影响因子：
2.8
通讯作者：
S. Keller
中科院分区：
文献类型：
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作者：
Gizem Korkmaz;Claire Kelling;Carol Robbins;S. Keller
本文通过对Python和R软件包的研究，开发了一些方法来估计影响开源软件（OSS）影响的因素，通过下载数量来衡量。OSS社区的特点是高水平的协作和共享，这导致了贡献者之间的交互，以及由于重用而导致的包之间的交互。我们使用从Depsy.org收集的有关Python和R包开发活动的数据，并生成依赖项和贡献者网络。我们使用网络特征以及作者和包属性为每个Python和R社区开发了三个准泊松模型。我们发现，一个包的衍生性越强（它拥有的依赖越多），它产生高影响的可能性就越小。我们还表明，依赖网络中包的中心性（由出度、接近中心性和pagerank衡量）对其影响有显着影响。此外，Python和R贡献者网络中开发人员的亲密度和加权度中心性起着重要作用。我们还发现，仅使用包特征（例如，作者的数量，提交的数量）将网络特征引入基线模型可以提高模型的性能。
This paper develops methods to estimate the factors that affect the impact of open-source software (OSS), measured by number of downloads, with a study of Python and R packages. The OSS community is characterized by a high level of collaboration and sharing which results in interactions between contributors as well as packages due to reuses. We use data collected from Depsy.org about the development activities of Python and R packages, and generate the dependency and contributor networks. We develop three Quasi-Poisson models for each of the Python and R communities using network characteristics, as well as author and package attributes. We find that the more derivative a package is (the more dependencies it has), the less likely it is to have a high impact. We also show that the centrality of a package in the dependency network measured by the out-degree, closeness centrality, and pagerank has a significant effect on its impact. Moreover, the closeness and weighted degree centralities of the developers in the Python and R contributor networks play an important role. We also find that introducing network features to a baseline model using only package features (e.g., number of authors, number of commits) improves the performance of the models.
影响因子：
3.3
作者：
Howison, James;Deelman, Ewa;Herbsleb, James D.
通讯作者：
Herbsleb, James D.