Context

: As soon as the concept of software ecosystem was proposed, it has aroused great interest in both academia and industry. Software ecosystem can be described as a special complex network. Community structures are critical towards understanding not only the network topology but also how the network functions. Traditional community detection algorithms in complex networks mainly utilize the network topology to measure the similarities between nodes. Because of the complexity of information interaction in software ecosystem, only considering the topology structure will lead to unreasonable division of communities.

Objective

: For solving community detection in software ecosystem more reasonably, we present a method of community detection by comprehensively evaluating developer cooperation intensity in software ecosystems.

Method

: First, we combine network topology information and developer interaction information to calculate the developer cooperation intensity, so as to deeply explore the relationship between developers from both topological and semantic properties. Then a community detection algorithm ABDCI is proposed based on the cooperation intensity of developers by referring to the hierarchical clustering idea of Louvain algorithm. Finally, this method is applied to many different types of developer networks in the software ecosystem through GitHub hosting platform.

Results

: Comparing with three classical community detection algorithms, we find that the proposed method can identify a clearer community structure for the developer collaboration network in the software ecosystem.

Conclusion

: Our approach provides an effective and extensible technique for solving the community detection problem of real developer collaboration network in software ecosystem. According to our findings, we conclude that community detection algorithms based on comprehensive topological properties and semantic properties are more suitable for real communities in software ecosystems than traditional single-property algorithms.

中文翻译：

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通过全面评估开发人员的合作强度在软件生态系统中进行社区检测

语境

：软件生态系统的概念一经提出，就引起了学术界和工业界的极大兴趣。软件生态系统可以描述为一个特殊的复杂网络。社区结构对于不仅了解网络拓扑而且还了解网络如何运行至关重要。复杂网络中的传统社区检测算法主要利用网络拓扑来度量节点之间的相似性。由于软件生态系统中信息交互的复杂性，仅考虑拓扑结构将导致社区的不合理划分。

目的

：为了更合理地解决软件生态系统中的社区检测问题，我们提出了一种通过全面评估软件生态系统中开发人员合作强度的社区检测方法。

方法

：首先，我们将网络拓扑信息和开发人员交互信息相结合，以计算开发人员合作强度，从而从拓扑和语义属性上深入探讨开发人员之间的关系。然后，参考开发商Louvin算法的层次聚类思想，基于开发者的协作强度，提出了一种社区检测算法ABDCI。最后，该方法通过GitHub托管平台应用于软件生态系统中的许多不同类型的开发人员网络。

结果

：与三种经典的社区检测算法相比，我们发现该方法可以为软件生态系统中的开发人员协作网络识别更清晰的社区结构。

结论

：我们的方法为解决软件生态系统中实际开发人员协作网络的社区检测问题提供了一种有效且可扩展的技术。根据我们的发现，我们得出结论，与传统的单属性算法相比，基于综合拓扑属性和语义属性的社区检测算法更适合软件生态系统中的实际社区。