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Detecting the functional interaction structure of software development teams.

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Summary
This summary is machine-generated.

This study introduces a data-driven method to map software team interaction structures using daily work traces. It helps teams understand and improve knowledge diffusion and overall performance.

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Area of Science:

  • Software Engineering
  • Team Dynamics
  • Organizational Behavior

Background:

  • Understanding team functional interaction structures is crucial for optimizing software development.
  • Existing methods often lack data-driven insights into real-time team interactions.
  • Identifying interaction patterns can reveal opportunities for improved collaboration and knowledge sharing.

Purpose of the Study:

  • To present a novel data-driven approach for detecting the functional interaction structure of software development teams.
  • To quantify and evaluate team interaction preferences based on daily work traces.
  • To provide insights for improving team performance and knowledge diffusion.

Main Methods:

  • Utilizing traces from development platforms to capture member activities.
  • Accounting for varying activity levels among team members.
  • Employing a block-constrained configuration model to compute role-based interaction preferences.

Main Results:

  • Successfully extracted the functional interaction structure of a software product team.
  • Validated the accuracy of the detected structure through member interviews.
  • Demonstrated the ability to compare team structures against benchmarks to identify improvement areas.

Conclusions:

  • The proposed approach offers an efficient and real-time method for analyzing and managing team interaction structures.
  • Quantifying functional interactions aids in understanding team dynamics and enhancing performance.
  • This technique can pinpoint specific areas for knowledge diffusion improvement within development teams.