Developer Social Network Research Articles

A community detection approach based on network representation learning for repository mining

In this paper, we propose a novel heterogeneous graph-based model for capturing and handling all the complex and strongly-correlated information of a software Developer Social Network (DSN) to support several analytic tasks. In particular, we challenge the problem of automatically discovering communities of software developers sharing interests for similar projects by relying on Social Network Analysis (SNA) findings. To overcome the huge graph-size issue, we leverage different graph embedding techniques. Eventually, we evaluate the proposed approach with respect to state-of-the-art approaches from an efficiency and an effectiveness point of view by carrying out an experiment involving the GitHub dataset.

Leveraging multifaceted proximity measures among developers in predicting future collaborations to improve the social capital of software projects

Social capital is an asset earned by people through their social connections. One of the motivations among developers to contribute to open source development and maintenance tasks is to earn social capital. Recent studies suggest that the social capital of the project has an impact on the sustained participation of the developers in open source software (OSS). One way to improve the social capital of the project is to help the developers in connecting with their peers. However, to the best of our knowledge, there is no prior research which attempts to predict future collaborations among developers and establish the significance of these collaborations on improving the social capital at the project level. To address this research gap, in this paper, we model the past collaborations among developers on version control system (VCS) and issue tracking system (ITS) as homogeneous and heterogeneous developer social network (DSN). Along with the novel path count based features, defined on proposed heterogeneous DSN, multifaceted proximity features are used to generate a feature set for machine learning classifiers. Our experiments performed on 5 popular open source projects (Spark, Kafka, Flink, WildFly, Hibernate) indicate that the proposed approach can predict the future collaborations among developers on both the platforms i.e. VCS as well as ITS with a significant accuracy (AUROC up to 0.85 and 0.9 for VCS and ITS respectively). A generic metric- recall of gain in social capital is proposed to investigate the efficacy of these predicted collaborations in improving the social capital of the project. We also concretised this metric on various measures of social capital and found that collaborations predicted by our approach have significant potential to improve the social capital at project level (e.g. Recall of gain in cohesion index up to 0.98 and Recall of gain in average godfather index up to 0.99 for VCS). We also showed that structure of collaboration network has an impact on the accuracy and usefulness of predicted collaborations. Since the past research suggests that many newcomers abandon the open source project due to social barriers which they face after joining the project, our research outcomes can be used to build the recommendation systems which might help to retain such developers by improving their social ties based on similar skills/interests.

Mining social collaboration patterns in developer social networks

Software development is extremely complex, requiring collaboration between teams and developers who collaborate on various tasks; these activities lead to the generation of an implicit developer social network (DSN). The authors’ aim to understand the development process in terms of collaboration between developers. In this work, they conducted an empirical study on mining social collaboration patterns of DSNs for open source software projects based on an integrated approach involving the identification of global and local collaboration patterns among developers based on social network analysis. The bug tracking system-based DSN (BTS-DSN) is chosen as an example over the other DSNs since it incorporates larger collaboration activities and actors. The empirical results show that the DSNs, specifically BTS-DSN, exhibits three different coordination pattern levels (Plan, Aware, and Reflexive) based on their collaboration activities. The mean time to repair metric proves that the Reflexive level occupies the fastest bug fixing time, then the Plan level comes secondly, and lastly the Aware level. In addition, each level group shows different collaboration behaviours among developers; thus, this information can be useful as a resource for better understanding of developer collaboration and collaboration awareness.

Empirical Study on the Evolution of Developer Social Networks

Software development is incredibly complex. Specifically, open-source software (OSS) development requires developers to collaborate with each other to conduct their work. Because software systems are evolving with time, collaboration among software developers may affect the quality of evolved software. The OSS developer teams collaborate in various tasks, including communications, coordination, and making various social collaboration in the OSS projects (e.g., bug/issue report, discussion, code revisions, and so on) without access restriction, and all these activities are used to generate an implicit developer social network (DSN). The DSN that is based on a bug tracking system is one of the most important DSNs that reflect the real collaboration between developers. As the software system evolves, the DSN evolves. This paper describes an empirical study of the evolution of DSNs on OSS projects collected from GitHub. Four perspectives were used: social network analysis, DSN as an ecosystem, community evolution patterns, and the core-periphery structure. The results demonstrated the DSNs over time have followed the power law degree distribution with +1% or more as an increasing rate to be more fitting overtime. DSNs over time are considered a small-world community. DSNs over time exhibits about 55% diversity with 75% of evenness between the developers to contribute in different OSS projects in the same environment. Moreover, DSNs over time have a few developers as core members and large developers as peripheral members. Finally, about 10% of developers changed their positions frequently over time.