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.

We present HyperCRX, a browser extension designed to enhance the open source experience by providing insights into GitHub projects and developers. Our tool seamlessly integrates with GitHub, offering interactive features that reveal the maintenance status of projects, the activity level of developers, and the connections between projects or developers. To ensure optimal performance, we have developed a special feature loading mechanism that supports the smooth operation of the existing 14 features. HyperCRX is now available on Google Chrome and Microsoft Edge, boasting a significant user base of 1,000 users on each browser. Our extension caters to a diverse range of users, including software engineers, project maintainers, open source newcomers, and company employers. With HyperCRX, users can make informed decisions and gain valuable insights to facilitate their engagement in the open source community. The source code is available on GitHub at https://github.com/hypertrons/hypertrons-crx, and a video screencast demonstrating the features can be found at https://youtu.be/_zm3FfpnZ28.

In this work, we introduce OpenGalaxy - an interactive exploration platform tool for a visualized GitHub Full Domain collaboration network based on 3D force-oriented layouts. We first collected Github domain-wide log data, built a developer-repository heterogeneous collaboration network, calculated both the Influence value for each repository and the activity value for each developer, finally performed visualization of the collaboration network and set up an 3D game-like interaction patterns.

Open-source software (OSS) plays an increasingly significant role in modern software development tendency, so accurate prediction of the future development of OSS has become an essential topic. The behavioral data of different open-source software are closely related to their development prospects. However, most of these behavioral data are typical high-dimensional time series data streams with noise and missing values. Hence, accurate prediction on such cluttered data requires the model to be highly scalable, which is not a property of traditional time series prediction models. To this end, we propose a temporal autoregressive matrix factorization (TAMF) framework that supports data-driven temporal learning and prediction. Specifically, we first construct a trend and period autoregressive model to extract trend and period features from OSS behavioral data, and then combine the regression model with a graph-based matrix factorization (MF) to complete the missing values by exploiting the correlations among the time series data. Finally, use the trained regression model to make predictions on the target data. This scheme ensures that TAMF can be applied to different types of high-dimensional time series data and thus has high versatility. We selected ten real developer behavior data from GitHub for case analysis. The experimental results show that TAMF has good scalability and prediction accuracy.

Open source software (OSS) on GitHub is experiencing continued growth and a rapid rise in the creation of projects. As OSS evolved, numerous projects faced decline, with some inevitably degrading into unmaintained status and getting archived by the owners. Understanding the deprecated projects helps deepen the perception of OSS maintenance and evolution. This paper describes the study of 361 popular GitHub projects that have been archived. By reading repository READMEs and sending surveys to project maintainers, we found the software repositories were archived due to being transferred, evolved, or unmaintained. We provide a set of 16 reasons and 10 practices to describe why and how these projects were archived. We further reveal the OSS development activity lifecycle patterns by fitting their commit history curves. We also confirmed the importance of the bus factor risk that would influence the sustainability of open source projects. Archiving a software repository is an explicit indication of repository deprecation. By providing the reason (the why), the strategy (the how), and the lifecycle patterns (the what) of the archived projects, we bring implications and practices that promote the health and sustainability of open source projects and the ecosystem overall.

Apart from being a code hosting platform, GitHub is the place where large-scale open collaborations and contributions happen. Every minute, thousands of developers are submitting code, having discussions of issues or pull requests, with all user behaviors recorded in the GitHub Event Stream (GES). Exploration of the activities in the GES could help understand who is active, the way they work, the time when they are active and even their location. To this end, a large-scale analysis was initially performed based on the 0.86 billion event records generated in 2020. We extracted 902K active contributors out of 14 million GitHub accounts by observing their activity distribution, then explored their behavior distribution, active time in the day and week, and estimated time zone distributions on the basis of their circadian activity rhythm. To go deeper, a case study of 79 projects in CNCF and contrast analyses of different project maturity levels were conducted. Our results showed that from a macro perspective, bots are increasingly more active and can serve numerous projects. Contributors work on weekdays, and are globally more inclined toward the daytime working hours in the Americas and Europe. The time zone distribution also reveals that UTC+2 and UTC-4 have the most active contributors. A critical discovery was the validation and quantification of a high bus factor risk exists in the OSS ecosystem. Whether from a large group point of view or within specific projects, a rather small group of OSS contributors (less than 20%) undertook the majority of the work. The GES can provide a wealth of information about open source software (OSS). Our findings provide insights into global GitHub collaboration behaviors and may be of help for researchers and practitioners to further understand modern OSS ecosystem.

The rise of container technology has brought about profound changes in the data center, and many software have been transferred to micro-service deployment and delivery. Therefore, it is of a broad practical significance to optimize the startup, operation and maintenance of large-scale containers in a massive user environment. At present, the mainstream container technology represented by Docker has achieved great success, but there is still much room for improvement in image volume and resource sharing. We reviewed the development of virtualization technology, and clarify that lightweight virtualization technology is the future research direction, which is very important for data-sensitive applications. By establishing a library file sharing model, we explored the impact of the degree of sharing of library files on the maximum number of containers that can be launched. We present an ultra-lightweight container design that minimizes the container runtime environment that supports application execution by refining the granularity of operational resources. At the same time, we extract the library files and the executable binary files into a single layer, which realizes the maximum sharing of the host's memory resources among containers. Then, according to the above scheme, we implement an ultra-lightweight container management engine: Reg (runtime environment generation), and a Reg-based workflow is defined. Finally, we carried out a series of comparative experiments on mirror volume, startup speed, memory usage and container startup storm, verified the effectiveness of proposed method in the large-scale container environment.

An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly addressed is how to allocate edge computing resources to achieve two optimization objectives: 1) minimize the service cost measured by the service latency and the power consumption experienced by service clients; and 2) maximize the service capacity measured by the number of service clients that can offload their computation tasks in the long term. This paper formulates this long-term problem as a stochastic optimization problem and solves it with an online algorithm based on Lyapunov optimization. This NPhard problem is decomposed into three sub-problems, which are then solved with a suite of techniques. The experimental results show that our approach significantly outperforms two baseline approaches.

The rise of cloud computing and the Internet not only bring change on the data center, but also lead to transformation in software development, deployment, operation and maintenance. With the continuous improvement of the current cloud computing and the internet environment, how to make better use of cloud computing platform, and how to serve the users is a popular field of computer software is a big challenge. In recent years, with the further development of concepts like micro-services and containers, software will further step forward to the Cloudware. This paper discusses how to deploy Cloudware in cloud environment, and proposes a new method to construct the PaaS platform which can directly deploy software on the cloud without any modification, while achieving a new model by the browser services. By using micro-service architecture, we achieving good performance of extension, scalable deployment, faults tolerance and flexible configuration. Finally, we evaluate this method by constructing a complete framework and carrying out an interactive delay experiment that directly focuses on users' experience, which also shows the effectiveness of this method.

Software paradigm is a driving force for the evolution of software technology. With the continuous improvement in the current cloud computing and the Internet environment, software will develop further into Cloudware, which is emerging as a new software paradigm. This paper defines the concept of Cloudware, and discusses it in the context of software paradigm. Then, based on a loosely coupled von Neumann computing model, we propose a new method of constructing a Cloudware PaaS system which can directly deploy software into the cloud without any modification. By using micro-service architecture, we can achieve high performance, scalable deployment, faults tolerance and flexible configuration. Finally, we evaluate this method by carrying out an interactive delay experiment that directly focuses on users' experience, which shows the effectiveness of our method.

The increasing market share of electric vehicles (EVs) makes charging facilities indispensable infrastructure for integrating EVs into the future intelligent transportation systems and smart grid. One promising facility called fast charging reservation(FCR) system was recently proposed. It allows people to reserve fast chargers ahead of time. In this system, fast chargers are the most scarce resource instead of electricity. Thus how to allocate these charging points requires careful designing. A good allocation policy should 1) ensure charging points to be allocated to EV users who really value them, and 2) prevent users' private information, e.g., identity, personal agenda, residing area and etc., from being inferred. A simple combination of classic multi-item auction and user identity anonymization cannot satisfy both criteria simultaneously. To find such an allocation, in this paper we investigate the design of privacy-preserving auctions in FCR systems. Traditional privacy-preserving strategies such as cryptography could incur high computation and communication overhead and hence jeopardize the efficiency of allocation. To this end, we propose Auc2Reserve, a differentially private randomized auction. Auc2Reserve applies an improved approximate sampler and the belief propagation (BP) technique to accelerate the resource allocation and pricing process. As a result, it is much more computationally efficient than generic exponential differentially private mechanisms and other theoretical approximate implementations. Through theoretical analysis, we show that Auc2Reserve is ?-incentive compatible, individual rational and ?-differentially private. And it provides a close-form approximation ratio in social welfare of FCR systems. In addition, we also demonstrate the efficacy of Auc2Reserve in terms of social welfare and privacy leakage via numerical simulation.

Service management is becoming more and more important within the area of IT management. How to efficiently manage and organize service in complicated IT service environments with frequent changes is a challenging issue. IT service and the related information from different sources are characterized as diverse, incomplete, heterogeneous, and geographically distributed. It is hard to consume these complicated services without knowledge assistant. To address this problem, a systematic way (with proposed toolsets and process) is proposed to tackle the challenges of acquisition, structuring, and refinement of structured knowledge. An integrated knowledge process is developed to guarantee the whole engineering procedure which utilizes Bayesian networks (BNs) as the knowledge model. This framework can be successfully applied on key tasks in service management, such as problem determination and change impact analysis, and a real example of Cisco VoIP system is introduced to show the usefulness of this method.

In this paper, the framework of MapReduce is explored for large-scale multimedia data mining. Firstly, a brief overview of MapReduce and Hadoop is presented to speed up large-scale multimedia data mining. Then, the high-level theory and low-level implementation for several key computer vision technologies involved in this work are introduced, such as 2D/3D interest point detection, clustering, bag of features, and so on. Experimental results on image classification, video event detection and near-duplicate video retrieval are carried out on a five-node Hadoop cluster to demonstrate the efficiency of the proposed MapReduce framework for large-scale multimedia data mining applications.

ABSTRACT Cloud computing has become a scalable services consumption and delivery platform in the field of computer science. As more and more consumers delegate their tasks to cloud providers, service level agreements (SLAs) between consumers and providers emerge as a key aspect. Because of the dynamic nature of the cloud, continuous monitoring on quality‐of‐service attributes is necessary to enforce SLAs. In this paper, we propose a trust mechanism‐based task scheduling model for cloud computing. Referring to the trust relationship models of social persons, trust relationship is built among computing nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayesian cognitive method. Integrating the trustworthiness of nodes into a dynamic level scheduling algorithm, the trust dynamic level scheduling algorithm for cloud computing is proposed. Theoretical analysis and simulations prove that the proposed algorithm can efficiently meet the requirement of cloud computing workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a secure way in cloud environment. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. In fact, Semantic Web technologies must integrate with Web 2.0 services for both to leverage each other's strengths. This paper illustrates how Semantic Web technologies can support information integration and make it easy to create semantic mashups. An intelligent recommendation system for tourism is presented to show the efficiency of our method. Through the ontology of tourism, the system allows the integration of heterogeneous online travel information. An integrated knowledge process is developed to guarantee the whole engineering procedure. Based on the Bayesian network technique, the system recommends tourist attractions to a user by taking into account the travel behavior both of the user and of other users. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract People encounter more information than they can possibly use every day. But all information is not necessarily of equal value. In many cases, certain information appears to be better, or more trustworthy, than other information. And the challenge that most people then face is to judge which information is more credible. In this paper we propose a new problem calledCorroboration Trust, which studies how to find credible news events by seeking more than one source to verify information on a given topic. We design an evidence‐based corroboration trust algorithm calledTrustNewsFinder, which utilizes the relationships between news articles and related evidence information (person, location, time and keywords about the news). A news article is trustworthy if it provides many pieces of trustworthy evidence, and a piece of evidence is likely to be true if it is provided by many trustworthy news articles. Our experiments show thatTrustNewsFindersuccessfully finds true events among conflicting information and identifies trustworthy news better than the popular search engines. Copyright © 2009 John Wiley & Sons, Ltd.

Service management is becoming more and more important within the area of IT service management. How to efficiently manage and organize service in complicated IT environments with frequent changes is a challenging issue. Service and the related information from different sources are characterized as diverse, incomplete, heterogeneous, and geographically distributed. It is hard to consume these complicated data without knowledge assistant. To address this problem, a knowledge engineering framework is proposed to tackle the challenges of acquisition, structuring and refinement of structured knowledge regarding existing different unstructured information resource, and the Bayesian network is utilized as the knowledge model. This framework can be successfully applied on key tasks in service management, such as problem determination and change impact analysis. And a real example of Cisco VoIP system is introduced to show the usefulness of this method.

Traditional overlay network simulators provide accurate low-level models of the network hardware and protocols but are but none of them deal with the problem of trust in the large scale overlay networks. We tackle this problem by employing a trust overlay simulator, which offer a viable solution to simulate trustworthy behavior in overlay networks. With this simulator, we can exam varies kinds of the trust and reputation mechanisms in the overlay environment. We hope that this simulator will help move the overlay network closer to fulfilling its promise by developing and testing trust and reputation-based protocols on it