An Improved Approach to Identifying Key Classes in Weighted Software Network
An Improved Approach to Identifying Key Classes in Weighted Software Network
复制标题
加权软件网络中关键类识别的改进方法
DOI：
10.1155/2016/3858637
复制
发表时间：
2016-01-01
影响因子：
--
通讯作者：
He, Peng
中科院分区：
文献类型：
--
作者：
Ding, Yi;Li, Bing;He, Peng
为了帮助新手在软件系统的开发过程中更好地理解它，通常会优先考虑关键类，以便尽快集中精力。已经提出了许多措施来识别网络中的关键节点。作为一种成功地应用于评估学者生产率的度量，指数是否适合于识别加权软件网络中的关键类，目前还知之甚少。在本文中，我们引入了四个索引变量来识别三个开源软件项目(即Tomcat、Ant和Jung)上的关键类，并通过与现有中心性度量的比较来验证所提出的措施的可行性。实验结果表明，本文提出的方法不仅能够识别关键类，而且比常用的中心性度量方法(至少提高了0.215)具有更好的性能。此外，这一发现表明，由节点顶边的权重定义的Me-Weight整体表现最好。
To help the newcomers understand a software system better during its development, the key classes are in general given priority to be focused on as soon as possible. There are numerous measures that have been proposed to identify key nodes in a network. As a metric successfully applied to evaluate the productivity of a scholar, little is known about whether -index is suitable to identify the key classes in weighted software network. In this paper, we introduced four -index variants to identify key classes on three open-source software projects (i.e., Tomcat, Ant, and JUNG) and validated the feasibility of proposed measures by comparing them with existing centrality measures. The results show that the measures proposed not only are able to identify the key classes but also perform better than some commonly used centrality measures (the improvement is at least 0.215). In addition, the finding suggests that mE-Weight defined by the weight of a node’s top edges performs best as a whole.