CIS strategy has been widely used in enterprise management,which brings economic and social benefits to enterprises.Based on the introduction of CIS strategic connotation,implementation effect and shaping cases,the pu...CIS strategy has been widely used in enterprise management,which brings economic and social benefits to enterprises.Based on the introduction of CIS strategic connotation,implementation effect and shaping cases,the purpose of this paper is to improve the image of adult education community,strengthen the construction and management level of community,enhance the attraction of community,and promote the high quality development of adult open education.展开更多
Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than eve...Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests (PRs) in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.展开更多
基金Zhejiang Province Education Science Planning 2020 Planning Project(Project No.:2020 SCG134).
文摘CIS strategy has been widely used in enterprise management,which brings economic and social benefits to enterprises.Based on the introduction of CIS strategic connotation,implementation effect and shaping cases,the purpose of this paper is to improve the image of adult education community,strengthen the construction and management level of community,enhance the attraction of community,and promote the high quality development of adult open education.
基金Project(2016-YFB1000805)supported by the National Grand R&D Plan,ChinaProjects(61502512,61432020,61472430,61532004)supported by the National Natural Science Foundation of China
文摘Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests (PRs) in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.