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International Conference on Product-Focused Software Process Improvement

PROFES 2016: Product-Focused Software Process Improvement pp 379–386Cite as

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

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 10027)

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.

Keywords

  • Information Flow Analysis
  • Social Network Analysis
  • Exchange Measurements
  • Applying Centrality Measures
  • Flow Method

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    Refer to [16] for a detailed description of the FLOW syntax.

  2. 2.

    UCINET is a common used tool for social network analysis.

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Acknowledgements

This work was supported by the German Federal Ministry of Education and Research under grant number K3: FKZ 13N13548 (2015-2018) and by the German Research Foundation (DFG) under grant number 263807701 (Project TeamFLOW, 2015-2017).

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Authors and Affiliations

  1. Software Engineering Group, Leibniz Universität Hannover, Hannover, Germany

    Stephan Kiesling, Jil Klünder & Kurt Schneider

  2. University of Bamberg, Bamberg, Germany

    Diana Fischer & Kai Fischbach

Authors
  1. Stephan Kiesling
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  2. Jil Klünder
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  3. Diana Fischer
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  4. Kurt Schneider
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  5. Kai Fischbach
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Corresponding author

Correspondence to Stephan Kiesling .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Pekka Abrahamsson

  2. Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany

    Andreas Jedlitschka

  3. and Technology, Norwegian University of Science and Technology, Trondheim, Norway

    Anh Nguyen Duc

  4. University of Innsbruck, Innsbruck, Austria

    Michael Felderer

  5. Okayama Prefectural University, Soja, Japan

    Sousuke Amasaki

  6. Tampere University of Technology, Tampere, Finland

    Tommi Mikkonen

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Kiesling, S., Klünder, J., Fischer, D., Schneider, K., Fischbach, K. (2016). Applying Social Network Analysis and Centrality Measures to Improve Information Flow Analysis. In: Abrahamsson, P., Jedlitschka, A., Nguyen Duc, A., Felderer, M., Amasaki, S., Mikkonen, T. (eds) Product-Focused Software Process Improvement. PROFES 2016. Lecture Notes in Computer Science(), vol 10027. Springer, Cham. https://doi.org/10.1007/978-3-319-49094-6_25

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  • DOI: https://doi.org/10.1007/978-3-319-49094-6_25

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