Applying Social Network Analysis and Centrality Measures to Improve Information Flow Analysis

被引:2
|
作者
Kiesling, Stephan [1 ]
Kluender, Jil [1 ]
Fischer, Diana [2 ]
Schneider, Kurt [1 ]
Fischbach, Kai [2 ]
机构
[1] Leibniz Univ Hannover, Software Engn Grp, Hannover, Germany
[2] Univ Bamberg, Bamberg, Germany
关键词
D O I
10.1007/978-3-319-49094-6_25
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In software development projects, documents are very important for sharing requirements and other information among employees. However, information can be transported in different ways. Conversations, meetings, workshops and emails convey and impart information as well. Especially large companies struggle in dealing with unclear and incorrect information flows. These information flows can be improved by means of information flow analysis and flow patterns. One technique to analyze information flows is the FLOW method. It supports visualization and analysis of information flows to detect lacks and anomalies and thereby improves information flows. An analyst gathers information transported in the company. Afterwards, information flows are visualized and analyzed based on patterns and personal experience. Nevertheless, analysis based on individual knowledge is error-prone. Hence, we improve the FLOW method with the help of social network analysis applying centrality measures to the FLOW method and to support the FLOW analyst.
引用
收藏
页码:379 / 386
页数:8
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