Abstract
With the wide adoption of social collaborative coding, more and more developers participate and collaborate on platforms such as GitHub through rich social and technical relationships, forming a large-scale complex technical system. Like the functionalities of critical nodes in other complex systems, influential developers and projects usually play an important role in driving this technical system to more optimized states with higher efficiency for software development, which makes it a meaningful research direction on identifying influential developers and projects in social collaborative coding platforms. However, traditional ranking methods seldom take into account the continuous interactions and the driving forces of human dynamics. In this paper, we combine the bursty interactions and the bipartite network structure between developers and projects and propose the BurstBiRank model. Firstly, the burstiness between each pair of developers and projects is calculated. Secondly, a weighted developer-project bipartite network is constructed using the burstiness as weight. Finally, an iterative score diffusion process is applied to this bipartite network and a final ranking score is obtained at the stationary state. The real-world case study on GitHub demonstrates the effectiveness of our proposed BurstBiRank and the outperformance of traditional ranking methods.
Highlights
Social collaborative coding is a popular paradigm among software developers, and collaborations of developers from all over the world can be conducted with the social and technical functionalities provided by such kind of platforms like GitHub
It is known that critical nodes usually play important role in operation management and optimization of complex systems. e same goes for complex technical systems such as GitHub, which is usually driven by influential developers and projects to more optimized states with higher efficiency for software development
In addition to burstiness-weighted developer-project bipartite network, two other developerproject bipartite networks are constructed with unweighted edge (UW) and commit number-weighted edge (CN), and all baseline methods are evaluated on these two bipartite networks with corresponding suffix such as PageRank-UW. e hyperparameters of BurstBiRank c and λ are both set to 0.85, and the query vectors u0 and p0 are set to the degrees of developers and projects over total number of nodes of each type, respectively, which can be calculated using equations (13) and (14)
Summary
Social collaborative coding is a popular paradigm among software developers, and collaborations of developers from all over the world can be conducted with the social and technical functionalities provided by such kind of platforms like GitHub. E same goes for complex technical systems such as GitHub, which is usually driven by influential developers and projects to more optimized states with higher efficiency for software development. In addition to direct collaboration, developers always seek popular developers and projects for improving coding ability and technical selection, which in turn makes collaborations more efficient. Us, identifying influential developers and projects is of great significance for the improvement of developer’s ability and the prosperity of open source community and has important applications in service recommendations [1, 2] and quality of service prediction [3,4,5,6].
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