Locating Community Smells in Software Development Processes Using
Higher-Order Network Centralities
- URL: http://arxiv.org/abs/2309.07467v1
- Date: Thu, 14 Sep 2023 06:48:15 GMT
- Title: Locating Community Smells in Software Development Processes Using
Higher-Order Network Centralities
- Authors: Christoph Gote, Vincenzo Perri, Christian Zingg, Giona Casiraghi,
Carsten Arzig, Alexander von Gernler, Frank Schweitzer, Ingo Scholtes
- Abstract summary: Community smells are negative patterns in software development teams' interactions that impede their ability to create software.
Current approaches aim to detect community smells by analysing static network representations of software teams' interaction structures.
We show that higher-order network models provide a robust means of revealing such hidden patterns and complex relationships.
- Score: 38.72139150402261
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Community smells are negative patterns in software development teams'
interactions that impede their ability to successfully create software.
Examples are team members working in isolation, lack of communication and
collaboration across departments or sub-teams, or areas of the codebase where
only a few team members can work on. Current approaches aim to detect community
smells by analysing static network representations of software teams'
interaction structures. In doing so, they are insufficient to locate community
smells within development processes. Extending beyond the capabilities of
traditional social network analysis, we show that higher-order network models
provide a robust means of revealing such hidden patterns and complex
relationships. To this end, we develop a set of centrality measures based on
the MOGen higher-order network model and show their effectiveness in predicting
influential nodes using five empirical datasets. We then employ these measures
for a comprehensive analysis of a product team at the German IT security
company genua GmbH, showcasing our method's success in identifying and locating
community smells. Specifically, we uncover critical community smells in two
areas of the team's development process. Semi-structured interviews with five
team members validate our findings: while the team was aware of one community
smell and employed measures to address it, it was not aware of the second. This
highlights the potential of our approach as a robust tool for identifying and
addressing community smells in software development teams. More generally, our
work contributes to the social network analysis field with a powerful set of
higher-order network centralities that effectively capture community dynamics
and indirect relationships.
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