Details
| Original language | English |
|---|---|
| Title of host publication | Product-Focused Software Process Improvement |
| Subtitle of host publication | 17th International Conference, PROFES 2016, Proceedings |
| Editors | Sousuke Amasaki, Tommi Mikkonen, Michael Felderer, Pekka Abrahamsson, Anh Nguyen Duc, Andreas Jedlitschka |
| Place of Publication | Cham |
| Publisher | Springer International Publishing AG |
| Pages | 379-386 |
| Number of pages | 8 |
| ISBN (electronic) | 978-3-319-49094-6 |
| ISBN (print) | 9783319490939 |
| Publication status | Published - 2016 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10027 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
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.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Product-Focused Software Process Improvement: 17th International Conference, PROFES 2016, Proceedings. ed. / Sousuke Amasaki; Tommi Mikkonen; Michael Felderer; Pekka Abrahamsson; Anh Nguyen Duc; Andreas Jedlitschka. Cham: Springer International Publishing AG, 2016. p. 379-386 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10027 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Applying Social Network Analysis and Centrality Measures to Improve Information Flow Analysis
AU - Kiesling, Stephan
AU - Klünder, Jil
AU - Fischer, Diana
AU - Schneider, Kurt
AU - Fischbach, Kai
N1 - Funding Information: 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).
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84998706946&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-49094-6_25
DO - 10.1007/978-3-319-49094-6_25
M3 - Conference contribution
SN - 9783319490939
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 379
EP - 386
BT - Product-Focused Software Process Improvement
A2 - Amasaki, Sousuke
A2 - Mikkonen, Tommi
A2 - Felderer, Michael
A2 - Abrahamsson, Pekka
A2 - Duc, Anh Nguyen
A2 - Jedlitschka, Andreas
PB - Springer International Publishing AG
CY - Cham
ER -