Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 12th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2016 |
| Place of Publication | New York, NY, USA |
| Publisher | Association for Computing Machinery (ACM) |
| ISBN (print) | 978-1-4503-4772-3 |
| Publication status | Published - 9 Sept 2016 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Abstract
Communication is an essential part of software product development. Therefore, communication is an inevitable means for information sharing. For example, ill-communicated requirements, guidelines or decisions complicate working in a team and may threaten project success. Hence, monitoring communication behavior can help fostering project success by preventing loss of information due to insufficient communication. Knowledge about a team’s communication behavior and information sharing enables the corresponding project leader to react. Forecasting communication behavior can indicate critical situations like too little communication, inappropriate media or wrong receivers at early project stages. A good forecast can identify if there is a need to change communication behavior. In a study with 165 students in 34 teams participating in a software project, we collected data concerning the used communication channels and perceived intensity. We combine these two parameters for analyzing and forecasting communication behavior. Considering the displayed evolution of communication behavior within a team can indicate the necessity to intervene. For example, the project leader can establish one more meeting each week to support information exchange. Our forecasting algorithm bases on k-nearest neighbor selection in order to identify comparable projects. We validate this approach using cross validation, which leads to an average accuracy of 90%. This level of accuracy may provide a reliable forecast and a good opportunity for early conflict identification.
Keywords
- Collaboration in teams, Communication, Data collection, Forecasting, Software development, Student software projects
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Networks and Communications
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Proceedings of the 12th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2016. New York, NY, USA: Association for Computing Machinery (ACM), 2016. 2972961 (ACM International Conference Proceeding Series).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Forecasting Communication Behavior in Student Software Projects
AU - Klünder, Jil
AU - Karras, Oliver
AU - Kortum, Fabian
AU - Schneider, Kurt
N1 - Funding Information: This work was funded by the German Research Foundation (DFG) under grant number 263807701 (Project Team-FLOW, 2015-2017). Publisher Copyright: © 2016 ACM. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2016/9/9
Y1 - 2016/9/9
N2 - Communication is an essential part of software product development. Therefore, communication is an inevitable means for information sharing. For example, ill-communicated requirements, guidelines or decisions complicate working in a team and may threaten project success. Hence, monitoring communication behavior can help fostering project success by preventing loss of information due to insufficient communication. Knowledge about a team’s communication behavior and information sharing enables the corresponding project leader to react. Forecasting communication behavior can indicate critical situations like too little communication, inappropriate media or wrong receivers at early project stages. A good forecast can identify if there is a need to change communication behavior. In a study with 165 students in 34 teams participating in a software project, we collected data concerning the used communication channels and perceived intensity. We combine these two parameters for analyzing and forecasting communication behavior. Considering the displayed evolution of communication behavior within a team can indicate the necessity to intervene. For example, the project leader can establish one more meeting each week to support information exchange. Our forecasting algorithm bases on k-nearest neighbor selection in order to identify comparable projects. We validate this approach using cross validation, which leads to an average accuracy of 90%. This level of accuracy may provide a reliable forecast and a good opportunity for early conflict identification.
AB - Communication is an essential part of software product development. Therefore, communication is an inevitable means for information sharing. For example, ill-communicated requirements, guidelines or decisions complicate working in a team and may threaten project success. Hence, monitoring communication behavior can help fostering project success by preventing loss of information due to insufficient communication. Knowledge about a team’s communication behavior and information sharing enables the corresponding project leader to react. Forecasting communication behavior can indicate critical situations like too little communication, inappropriate media or wrong receivers at early project stages. A good forecast can identify if there is a need to change communication behavior. In a study with 165 students in 34 teams participating in a software project, we collected data concerning the used communication channels and perceived intensity. We combine these two parameters for analyzing and forecasting communication behavior. Considering the displayed evolution of communication behavior within a team can indicate the necessity to intervene. For example, the project leader can establish one more meeting each week to support information exchange. Our forecasting algorithm bases on k-nearest neighbor selection in order to identify comparable projects. We validate this approach using cross validation, which leads to an average accuracy of 90%. This level of accuracy may provide a reliable forecast and a good opportunity for early conflict identification.
KW - Collaboration in teams
KW - Communication
KW - Data collection
KW - Forecasting
KW - Software development
KW - Student software projects
UR - http://www.scopus.com/inward/record.url?scp=84999004888&partnerID=8YFLogxK
U2 - 10.1145/2972958.2972961
DO - 10.1145/2972958.2972961
M3 - Conference contribution
SN - 978-1-4503-4772-3
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 12th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2016
PB - Association for Computing Machinery (ACM)
CY - New York, NY, USA
ER -