TY - GEN

T1 - Don’t underestimate the human factors! exploring team communication effects

AU - Kortum, Fabian

AU - Klünder, Jil

AU - Schneider, Kurt

N1 - Funding information: Acknowledgment. This work was funded by the German Research Foundation (DFG) under grant number 263807701 (Project TeamFLOW, 2015–2017).

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Team communication addresses a critical issue for software developments. Understanding human behavior and communication take an important role for cost optimized scheduling and adjustment of dysfunctional manner. But team phenomena are often not trivial to interpret. Empirical studies can disclose practical information. Many kinds of research with the focus on human factors justify findings solely through linear statistics. This leads to an estimation problem of formally interpreted effects, in particular for diagnosis models. In this paper, we investigate several team communication effects with data records from an empirical study with 34 academic software projects. In general, we want to increase the awareness for often insufficiently interpreted human factors. We apply conventional linear correlation statistics in comparison with the novel exploratory analysis MINE on three sample cases concerning team meetings and communication behavior. Both analyzing techniques approved to be capable in identifying the relevant team communication effects within the case studies, even though with different estimation of relevances. The study demonstrates how quickly e.g. group behavior and communication effects can be insufficiently interpreted with dangerous gaps for factor estimation in modeling approaches.

AB - Team communication addresses a critical issue for software developments. Understanding human behavior and communication take an important role for cost optimized scheduling and adjustment of dysfunctional manner. But team phenomena are often not trivial to interpret. Empirical studies can disclose practical information. Many kinds of research with the focus on human factors justify findings solely through linear statistics. This leads to an estimation problem of formally interpreted effects, in particular for diagnosis models. In this paper, we investigate several team communication effects with data records from an empirical study with 34 academic software projects. In general, we want to increase the awareness for often insufficiently interpreted human factors. We apply conventional linear correlation statistics in comparison with the novel exploratory analysis MINE on three sample cases concerning team meetings and communication behavior. Both analyzing techniques approved to be capable in identifying the relevant team communication effects within the case studies, even though with different estimation of relevances. The study demonstrates how quickly e.g. group behavior and communication effects can be insufficiently interpreted with dangerous gaps for factor estimation in modeling approaches.

KW - Data visualization

KW - Exploratory analysis

KW - Human factors

KW - Interpretation problem

KW - Team communication

UR - http://www.scopus.com/inward/record.url?scp=85034594366&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-69926-4_36

DO - 10.1007/978-3-319-69926-4_36

M3 - Conference contribution

AN - SCOPUS:85034594366

SN - 9783319699257

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 457

EP - 469

BT - Product-Focused Software Process Improvement -18th International Conference, PROFES 2017, Proceedings

A2 - Turhan, Burak

A2 - Winkler, Dietmar

A2 - Sarro, Federica

A2 - Kalinowski, Marcos

A2 - Mendez Fernandez, Daniel

A2 - Felderer, Michael

PB - Springer Verlag

T2 - 18th International Conference on Product-Focused Software Process Improvement, PROFES 2017

Y2 - 29 November 2017 through 1 December 2017

ER -