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GPSA:A Greedy Pull-based Scheduling Approach for P2P Live Streaming under Heterogeneous Environments 认领 引用 被引量：2
1
作者 Chen Wei Su Sen +1 位作者 Yang Fangchun Shuang Kai 《China Communications》 SCIE 2010年第2期42-52,共11页
Pull-based P2P live streaming is a promising solution for the large scale streaming systems,like PPStream,PPlive,due to its high scalability,low cost and high resilience.However,they usually suffer from bad delay perf... Pull-based P2P live streaming is a promising solution for the large scale streaming systems,like PPStream,PPlive,due to its high scalability,low cost and high resilience.However,they usually suffer from bad delay performance.In this paper,we seek to improve the delay performance under ensuring video display quality stemming from chunk scheduling.And so we model Pull-based chunk scheduling problem as a multi-objective optimization problem to minimize the video delay and maximize video display quality in the environment of heterogeneous upload bandwidths,heterogeneous and dynamic propagation delays.Finally we put up with a greedy Pull-based scheduling approach(GPSA)to solve the optimization problem.The evaluation shows GPSA can outperform two classical chunk scheduling approaches and adapt to dynamic variance of propagation delays. 展开更多
关键词 P2P live streaming pull-based scheduling heterogeneous environments delay performaDce
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CTCPPre: A prediction method for accepted pull requests in GitHub 认领 引用 被引量：5
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作者 JIANG Jing ZHENG Jia-teng +1 位作者 YANG Yun ZHANG Li 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第2期449-468,共20页
As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests.An accepted pull request ... As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests.An accepted pull request prediction approach can help integrators by allowing them either to enforce an immediate rejection of code changes or allocate more resources to overcome the deficiency.In this paper,an approach CTCPPre is proposed to predict the accepted pull requests in GitHub.CTCPPre mainly considers code features of modified changes,text features of pull requests’description,contributor features of developers’previous behaviors,and project features of development environment.The effectiveness of CTCPPre on 28 projects containing 221096 pull requests is evaluated.Experimental results show that CTCPPre has good performances by achieving accuracy of 0.82,AUC of 0.76 and F1-score of 0.88 on average.It is compared with the state of art accepted pull request prediction approach RFPredict.On average across 28 projects,CTCPPre outperforms RFPredict by 6.64%,16.06%and 4.79%in terms of accuracy,AUC and F1-score,respectively. 展开更多
关键词 accepted pull request prediction code review GitHub pull-based software development
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CoreDevRec: Automatic Core Member Recommendation for Contribution Evaluation 认领 引用 被引量：10
3
作者 蒋竞 贺佳欢 陈学渊 《Journal of Computer Science & Technology》 SCIE EI CSCD 2015年第5期998-1016,共19页
The pull-based software development helps developers make contributions flexibly and efficiently. Core members evaluate code changes submitted by contributors, and decide whether to merge these code changes into repos... The pull-based software development helps developers make contributions flexibly and efficiently. Core members evaluate code changes submitted by contributors, and decide whether to merge these code changes into repositories or not. Ideally, code changes are assigned to core members and evaluated within a short time after their submission. However, in reality, some popular projects receive many pull requests, and core members have difficulties in choosing pull requests which are to be evaluated. Therefore, there is a growing need for automatic core member recommendation, which improves the evaluation process. In this paper, we investigate pull requests with manual assignment. Results show that 3.2%～40.6% of pull requests are manually assigned to specific core members. To assist with the manual assignment, we propose CoreDevRec to recommend core members for contribution evaluation in GitHub. CoreDevRec uses support vector machines to analyze different kinds of features, including file paths of modified codes, relationships between contributors and core members, and activeness of core members. We evaluate CoreDevRec on 18 651 pull requests of five popular projects in GitHub. Results show that CoreDevRec achieves accuracy from 72.9% to 93.5% for top 3 recommendation. In comparison with a baseline approach, CoreDevRec improves the accuracy from 18.7% to 81.3% for top 3 recommendation. Moreover, CoreDevRec even has higher accuracy than manual assignment in the project TrinityCore. We believe that CoreDevRec can improve the assignment of pull requests. 展开更多
关键词 core member recommendation contribution evaluation pull-based software development
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ForkXplorer:an approach of fork summary generation 认领 引用
4
作者 Zhang ZHANG Xinjun MAO +1 位作者 Chao ZHANG Yao LU 《Frontiers of Computer Science》 SCIE EI CSCD 2022年第2期1-15,共15页
Pull-based development has become an important paradigm for distributed software development.In this model,each developer independently works on a copied repository(i.e.,a fork)from the central repository.It is essent... Pull-based development has become an important paradigm for distributed software development.In this model,each developer independently works on a copied repository(i.e.,a fork)from the central repository.It is essential for developers to maintain awareness of the state of other forks to improve collaboration efficiency.In this paper,we propose a method to automatically generate a summary of a fork.We first use the random forest method to generate the label of a fork,i.e.,feature implementation or a bug fix.Based on the information of the fork-related commits,we then use the TextRank algorithm to generate detailed activity information of the fork.Finally,we apply a set of rules to integrate all related information to construct a complete fork summary.To validate the effectiveness of our method,we conduct 30 groups of manual experiment and 77 groups of case studies on Github.We propose Feaavgto evaluate the performance of Feaavgthe generated fork summary,considering the content accuracy,content integrity,sentence fluency,and label extraction accuracy.The results show that the average of of the fork summary generated by this method is 0.672.More than 63%of project maintainers and the contributors believe that the fork summary can improve development efficiency. 展开更多
关键词 open source software pull-based development fork summary distributed cooperative development
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