The rapid development and widespread adoption of open source software, facilitated and accelerated by the web, have fostered a vibrant ecosystem for collaborative development and innovation. GitHub, a leading platform for collaborative software development, currently hosts more than 100 million registered users, creating a substantial ecosystem for examining open source community behaviors. Existing tools for measuring open source communities primarily focus on metrics such as issue response time, pull request response time, or incremental stars to provide insights into community activity. However, these tools are limited in their ability to assess the influence of communities from the perspective of collaboration networks. Moreover, current data collection solutions offer fixed functionalities and lack the flexibility to support multi-source, fine-grained, and customizable data acquisition, which is essential for comprehensive analysis of Open Source Ecosystems (OSEs). In this paper, we present OpenDigger, a framework for multi-dimensional assessment of collaboration activities in OSEs. To enable scalable, modular, and continuous acquisition of OSE data, we developed OpenCrawler, a one-line service providing customizable, fine-grained control over data collection. Using the collected data, OpenDigger computes 20 statistical and 2 network-based metrics, and our empirical analysis further verifies their effectiveness in enabling a comprehensive assessment of trends in OSEs. By continuously collecting logs from GitHub and Gitee, OpenDigger has now accumulated over 9 billion records. Our framework has already been deployed across multiple industrial environments, including Alibaba Group, Ant Group, Apache Foundation, and Mulan Open Source Community.

Context: On March 20, 2024, Redis Inc. changed the Redis project’s license from the permissive Berkeley Software Distribution (BSD) license to a dual licensing model under the Redis Source Available License (RSALv2) and the Server Side Public License (SSPLv1). The official rationale for this change was to restrict cloud service providers from offering Redis as a managed service without contributing back. The license transition drew widespread attention within the open source community and raised concerns from some developers and organizations about its implications for project governance, contribution dynamics, and long-term sustainability. Objective: Analyze the contribution distribution within the Redis repository by examining the behavior of developers with different motivations, evaluate the changes that occurred during the license change period, and assess whether cloud providers are exploiting the open-source project. Method: This study categorizes developers’ motivations based on collaboration behavior data. By leveraging developer profile information, contributors are categorized into three motivation types: company-driven, community-driven, and communication-driven, enabling an analysis of trends across different contributor groups. The study primarily relies on project evaluation metrics from the Community Health Analytics Open Source Software (CHAOSS) community, along with collaboration metrics, to examine changes within the Redis repository. Results: Since 2017, cloud providers have consistently contributed to the Redis open-source project. During the license change period, there was a notable decrease in contributor participation and influence, particularly among company-driven and community-driven developers. Several core contributors transitioned to the newly established Valkey project. Conclusion: Following the license change by Redis Inc., the community experienced a certain degree of fragmentation, with major cloud providers migrating to the Valkey fork. Cloud providers have recognized the importance of the community and are willing to invest resources into the open-source projects they participate in, ensuring better collaboration and alignment with upstream development for their cloud services.

Communities, revealing tightly-knit groups within networks, are essential to complex network analysis. While contrastive learning has been widely applied to community detection, existing studies often overlook latent community assignments information when constructing negative samples, resulting in many potential false negatives. To overcome this limitation, we propose Cluster-Perceptive Graph Contrastive Learning (CPGCL), which integrates the collaborative learning of intermediate representations and community assignments. Our approach utilizes a shared-parameter encoder to generate intermediate representations from two views. These representations are used to derive community probability distributions, which guide model training through contrastive loss between communities. Then by leveraging the generated sample community probability distributions to dynamically reweight the intermediate representations of sample pairs for contrastive learning, we mitigate the issue of potential false negatives and promote more discriminative intermediate representations. Additionally, high-confidence community assignments are used to iteratively refine the intermediate representations, further enhancing community detection performance. Experiments on three benchmark datasets validate the effectiveness and superiority of our approach. Code is available at https://github.com/l1tok/CPGCL.git.