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Locating community smells in software development processes using higher-order network centralities.

Christoph Gote1,2,3, Vincenzo Perri2,3, Christian Zingg1

  • 1Chair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, 8092 Zurich, Switzerland.

Social Network Analysis and Mining
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Summary
This summary is machine-generated.

This study introduces higher-order network models to detect hidden community smells in software development processes, improving team collaboration and identifying previously unknown issues for better software creation.

Keywords:
Centrality measuresCommunity smellsHigher-order networksPath analysisSocial debt

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Area of Science:

  • Computer Science
  • Software Engineering
  • Network Analysis

Background:

  • Community smells, such as isolation and poor communication, hinder software development team productivity.
  • Existing methods analyze static interaction networks, failing to pinpoint smells within dynamic development processes.
  • Higher-order network models offer a more robust approach to uncovering complex team interaction patterns.

Purpose of the Study:

  • To develop and validate higher-order network models for detecting community smells in software development.
  • To introduce novel centrality measures for identifying influential nodes within team interaction networks.
  • To apply these methods to a real-world software development team to identify and locate community smells.

Main Methods:

  • Developed centrality measures based on the MOGen higher-order network model.
  • Validated the effectiveness of these measures in predicting influential nodes using five empirical datasets.
  • Analyzed a product team at genua GmbH, identifying specific community smells within their development process.

Main Results:

  • The higher-order network model successfully identified critical community smells in two areas of the team's development process.
  • One identified community smell was known to the team, while the second was previously unrecognized.
  • Novel higher-order network centralities were shown to effectively capture community dynamics and indirect relationships.

Conclusions:

  • Higher-order network models provide a powerful tool for identifying and locating community smells in software development.
  • This approach can reveal hidden team interaction patterns and improve the software development process.
  • The developed centrality measures contribute significantly to the field of social network analysis for understanding community dynamics.