Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2016 10th International Conference on the Quality of Information and Communications Technology, QUATIC 2016 |
| Editors | Mark Paulk, Miguel A. Brito, Vasco Amaral, Ricardo J. Machado, Miguel Goulao |
| Pages | 166-171 |
| Number of pages | 6 |
| ISBN (electronic) | 9781509035816 |
| Publication status | Published - 11 Jan 2017 |
Abstract
Effective team communication is a prerequisite for software quality and project success. It implies correctly elicited customer requirements, conduction of occurring change requests and to adhere releases. Team communication is a complex construct that consists of numerous characteristics, individual styles, influencing factors and dynamic intensities during a project. These elements are complicated to be measured or scheduled, especially in newly formed teams. According to software developers with few experiences in teams, it would be highly desirable to recognize dysfunctional or underestimated communication behaviors already in early project phases. Otherwise, negative affects may cause delay of releases or even endanger software quality. We introduce an approach on the feasibility of forecasting team's communication behavior in student software projects. We build a very first forecasting model that involves software engineering and industrial psychological terms to extract multi week communication forecasts with accurate results. The model consists of a k-nearest neighbor machine learning algorithm and is trained and evaluated with 34 student software projects from a previously taken field study. This study is an encouraging first step towards forecasting team communication to reveal potential miscommunications during a project. It is our aim to give young software developing teams an experience-based assistance about their information flow and enable adjustment for dysfunctional communication, to avoid fire fighting situation or even risks of alternating software qualities.
Keywords
- Predictive Analytics, Product Qality, Real-Time Scheduling, Software Project Success, Team Communication
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Business, Management and Accounting(all)
- Management of Technology and Innovation
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Information Systems
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
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Proceedings - 2016 10th International Conference on the Quality of Information and Communications Technology, QUATIC 2016. ed. / Mark Paulk; Miguel A. Brito; Vasco Amaral; Ricardo J. Machado; Miguel Goulao. 2017. p. 166-171 7814540.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Early Diagnostics on Team Communication: Experience-Based Forecasts on Student Software Projects
AU - Kortum, Fabian
AU - Klünder, Jil
N1 - Publisher Copyright: © 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/1/11
Y1 - 2017/1/11
N2 - Effective team communication is a prerequisite for software quality and project success. It implies correctly elicited customer requirements, conduction of occurring change requests and to adhere releases. Team communication is a complex construct that consists of numerous characteristics, individual styles, influencing factors and dynamic intensities during a project. These elements are complicated to be measured or scheduled, especially in newly formed teams. According to software developers with few experiences in teams, it would be highly desirable to recognize dysfunctional or underestimated communication behaviors already in early project phases. Otherwise, negative affects may cause delay of releases or even endanger software quality. We introduce an approach on the feasibility of forecasting team's communication behavior in student software projects. We build a very first forecasting model that involves software engineering and industrial psychological terms to extract multi week communication forecasts with accurate results. The model consists of a k-nearest neighbor machine learning algorithm and is trained and evaluated with 34 student software projects from a previously taken field study. This study is an encouraging first step towards forecasting team communication to reveal potential miscommunications during a project. It is our aim to give young software developing teams an experience-based assistance about their information flow and enable adjustment for dysfunctional communication, to avoid fire fighting situation or even risks of alternating software qualities.
AB - Effective team communication is a prerequisite for software quality and project success. It implies correctly elicited customer requirements, conduction of occurring change requests and to adhere releases. Team communication is a complex construct that consists of numerous characteristics, individual styles, influencing factors and dynamic intensities during a project. These elements are complicated to be measured or scheduled, especially in newly formed teams. According to software developers with few experiences in teams, it would be highly desirable to recognize dysfunctional or underestimated communication behaviors already in early project phases. Otherwise, negative affects may cause delay of releases or even endanger software quality. We introduce an approach on the feasibility of forecasting team's communication behavior in student software projects. We build a very first forecasting model that involves software engineering and industrial psychological terms to extract multi week communication forecasts with accurate results. The model consists of a k-nearest neighbor machine learning algorithm and is trained and evaluated with 34 student software projects from a previously taken field study. This study is an encouraging first step towards forecasting team communication to reveal potential miscommunications during a project. It is our aim to give young software developing teams an experience-based assistance about their information flow and enable adjustment for dysfunctional communication, to avoid fire fighting situation or even risks of alternating software qualities.
KW - Predictive Analytics
KW - Product Qality
KW - Real-Time Scheduling
KW - Software Project Success
KW - Team Communication
UR - http://www.scopus.com/inward/record.url?scp=85013777476&partnerID=8YFLogxK
U2 - 10.1109/quatic.2016.043
DO - 10.1109/quatic.2016.043
M3 - Conference contribution
SP - 166
EP - 171
BT - Proceedings - 2016 10th International Conference on the Quality of Information and Communications Technology, QUATIC 2016
A2 - Paulk, Mark
A2 - Brito, Miguel A.
A2 - Amaral, Vasco
A2 - Machado, Ricardo J.
A2 - Goulao, Miguel
ER -