@article{cui_zhang_li_zhou_song_gehringer_2024, title={How Pre-class Programming Experience Influences Students' Contribution to Their Team Project: A Statistical Study}, url={https://doi.org/10.1145/3626252.3630870}, DOI={10.1145/3626252.3630870}, abstractNote={Group or team projects are an essential component of the software engineering curriculum. Earlier studies have explored how prior programming experience influences students' team project performance and overall class performance in software engineering. However, few studies address the impact of prior programming experience on students' contributions to team projects. Previous work has varied in its definitions of prior programming experience or skill, leading to inconsistent findings. In this study, we collected pre-class GitHub contribution metrics from 237 students (forming 79 teams of three) across two academic years to measure their prior programming experience and skills. We also mined students' project repositories' git logs to collect individual student contributions. A central question revolved around whether students with more substantial prior programming experience were indeed more active contributors to their project teams. Interestingly, our data indicated a positive correlation between prior programming experience and contributions to team projects. We further delved into team dynamics. Specifically, we questioned if teams made up of members with comparable skill levels exhibited a more even distribution of contributions. Contrary to expectations, our findings revealed no association between these two variables. Moreover, we investigated the team configurations that might encourage the rise of "free riders"-students who contributed only minimally. This paper seeks to augment the body of research on computing education and assist educators in understanding how prior programming experience impacts students' contributions in team projects.}, journal={PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1}, author={Cui, Jialin and Zhang, Runqiu and Li, Ruochi and Zhou, Fangtong and Song, Yang and Gehringer, Edward}, year={2024}, pages={255–261} }
 @article{cui_zhang_li_song_zhou_gehringer_2023, title={Correlating Students' Class Performance Based on GitHub Metrics: A Statistical Study}, url={https://doi.org/10.1145/3587102.3588799}, DOI={10.1145/3587102.3588799}, abstractNote={What skills does a student need to succeed in a programming class? Ostensibly, previous programming experience may affect a student's performance. Most past studies on this topic use self-reporting questionnaires to query students about their programming experience. This paper presents a novel, unified, and replicable way to measure previous programming experience using students' pre-class GitHub contributions. To our knowledge, we are the first to use GitHub contributions in this way. We conducted a comprehensive statistical study of students in an object-oriented design and development class from 2017 to 2022 (n = 751) to explore the relationships between GitHub contributions (commits, comments, pull requests, etc.) and students' performance on exams, projects, designs, etc. in the class. Several kinds of contributions were shown to have statistically significant correlations with performance in the class. A set of two-samplet -tests demonstrate statistical significance of the difference between the means of some contributions from the high-performing and low-performing groups.}, journal={PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL 1}, author={Cui, Jialin and Zhang, Runqiu and Li, Ruochi and Song, Yang and Zhou, Fangtong and Gehringer, Edward}, year={2023}, pages={526–532} }
 @article{wang_yu_yang_xue_gu_li_zhou_2023, title={Fence: Fee-Based Online Balance-Aware Routing in Payment Channel Networks}, volume={10}, ISSN={["1558-2566"]}, DOI={10.1109/TNET.2023.3324136}, abstractNote={Scalability is a critical challenge for blockchain-based cryptocurrencies. Payment channel networks (PCNs) have emerged as a promising solution for this challenge. However, channel balance depletion can significantly limit the capacity and usability of a PCN. Specifically, frequent transactions that result in unbalanced payment flows from two ends of a channel can quickly deplete the balance on one end, thus blocking future payments from that direction. In this paper, we propose Fence, an online balance-aware fee setting algorithm to prevent channel depletion and improve PCN sustainability and long-term throughput. In our algorithm, PCN routers set transaction fees based on the current balance and level of congestion on each channel, in order to incentivize payment senders to utilize paths with more balance and less congestion. Our algorithm is guided by online competitive algorithm design, and achieves an asymptotically tight competitive ratio with constant violation in a unidirectional PCN. We further prove that no online algorithm can achieve a finite competitive ratio in a general PCN. Extensive simulations under a real-world PCN topology show that Fence achieves high throughput and keeps network channels balanced, compared to state-of-the-art PCN routing algorithms.}, journal={IEEE-ACM TRANSACTIONS ON NETWORKING}, author={Wang, Xiaojian and Yu, Ruozhou and Yang, Dejun and Xue, Guoliang and Gu, Huayue and Li, Zhouyu and Zhou, Fangtong}, year={2023}, month={Oct} }
 @article{zhou_yu_li_gu_wang_2022, title={FedAegis: Edge-Based Byzantine-Robust Federated Learning for Heterogeneous Data}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM48099.2022.10000981}, abstractNote={This paper studies how an edge-based federated learning algorithm called FedAegis can be designed to be ro-bust under both heterogeneous data distributions and Byzantine adversaries. The divergence of local data distributions leads to suboptimal results for the training process of federated learning, and the Byzantine adversaries aim to prevent the training process from converging in a distributed learning system. In this paper, we show that an edge-based hierarchical federated learning architecture can help tackle this dilemma by utilizing edge nodes geographically close to clusters of local devices. By combining a distributionally robust global loss function with a local Byzantine-robust aggregation rule, FedAegis can defend against remote Byzantine adversaries who cannot manipulate local devices' connections to edge nodes, meanwhile accounting for global data heterogeneity across benign local devices. Experiments with the MNIST, FMNIST and CIFAR-IO datasets show that our proposed algorithm can achieve convergence and high accuracy under heterogeneous data and various attack scenarios, while state-of-the-art defenses and robustness mechanisms are non-converging or have reduced average and/or worst-case accuracy.}, journal={2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022)}, author={Zhou, Fangtong and Yu, Ruozhou and Li, Zhouyu and Gu, Huayue and Wang, Xiaojian}, year={2022}, pages={3005–3010} }
 @article{wang_gu_li_zhou_yu_yang_2022, title={Why Riding the Lightning? Equilibrium Analysis for Payment Hub Pricing}, ISSN={["1550-3607"]}, DOI={10.1109/ICC45855.2022.9839171}, abstractNote={Payment Channel Network (PCN) is an auspicious solution to the scalability issue of the blockchain, improving transaction throughput without relying on on-chain transactions. In a PCN, nodes can set prices for forwarding payments on behalf of other nodes, which motivates participation and improves network stability. Analyzing the price setting behaviors of PCN nodes plays a key role in understanding the economic properties of PCNs, but has been under-studied in the literature. In this paper, we apply equilibrium analysis to the price-setting game between two payment hubs in the PCN with limited channel capacities and partial overlap demand. We analyze existence of pure Nash Equilibriums (NEs) and bounds on the equilibrium revenue under various cases, and propose an algorithm to find all pure NEs. Using real data, we show bounds on the price of anarchy/stability and average transaction fee under realistic network conditions, and draw conclusions on the economic advantage of the PCN for making payment transfers by cryptocurrency users.}, journal={IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022)}, author={Wang, Xiaojian and Gu, Huayue and Li, Zhouyu and Zhou, Fangtong and Yu, Ruozhou and Yang, Dejun}, year={2022}, pages={5409–5414} }
 @article{yu_lo_zhou_xue_2021, title={Data-Driven Edge Resource Provisioning for Inter-Dependent Microservices with Dynamic Load}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM46510.2021.9685155}, abstractNote={This paper studies how to provision edge computing and network resources for complex microservice-based applications (MSAs) in face of uncertain and dynamic geo-distributed demands. The complex inter-dependencies between distributed microservice components make load balancing for MSAs extremely challenging, and the dynamic geo-distributed demands exacerbate load imbalance and consequently congestion and performance loss. In this paper, we develop an edge resource provisioning model that accurately captures the inter-dependencies between microservices and their impact on load balancing across both computation and communication resources. We also propose a robust formulation that employs explicit risk estimation and optimization to hedge against potential worst-case load fluctuations, with controlled robustness-resource trade-off. Utilizing a data-driven approach, we provide a solution that provides risk estimation with measurement data of past load geo-distributions. Simulations with real-world datasets have validated that our solution provides the important robustness crucially needed in MSAs, and performs superiorly compared to baselines that neglect either network or inter-dependency constraints.}, journal={2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)}, author={Yu, Ruozhou and Lo, Szu-Yu and Zhou, Fangtong and Xue, Guoliang}, year={2021} }