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Applying Social Network Analysis and Centrality Measures to Improve Information Flow Analysis
Kiesling, Stephan; Klünder, Jil; Fischer, Diana; u. a. (2016): Applying Social Network Analysis and Centrality Measures to Improve Information Flow Analysis, in: Pekka Abrahamsson, Andreas Jedlitschka, Anh Nguyen Duc, u. a. (Hrsg.), Product-Focused Software Process Improvement : 17th International Conference, PROFES 2016, Trondheim, Norway, November 22-24, 2016, Proceedings, Cham: Springer International Publishing, S. 379–386, doi: 10.1007/978-3-319-49094-6_25.
Faculty/Chair:
By:
... ; Fischer, Diana; ...
Title of the compilation:
Product-Focused Software Process Improvement : 17th International Conference, PROFES 2016, Trondheim, Norway, November 22-24, 2016, Proceedings
Corporate Body:
International Conference on Product-Focused Software Process Improvement
Publisher Information:
Year of publication:
2016
Pages:
Language:
English
Abstract:
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.
Type:
Contribution to an Articlecollection
Activation date:
June 26, 2017
Permalink
https://fis.uni-bamberg.de/handle/uniba/42078