Stochastic performance analysis of global software development teams

Ricardo M. Czekster, Paulo Fernandes, Lucelene Lopes, Afonso Sales, Alan R. Santos, Thais Webber

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Measuring productivity in globally distributed projects is crucial to improve team performance. These measures often display information on whether a given project is moving forward or starts to demonstrate undesired behaviors. In this paper we are interested in showing how analytical models could deliver insights for the behavior of specific distributed software collaboration projects. We present a model for distributed projects using stochastic automata networks (SAN) formalism to estimate, for instance, the required level of coordination for specific project configurations. We focus our attention on the level of interaction among project participants and its close relation with team's productivity. The models are parameterized for different scenarios and solved using numerical methods to obtain exact solutions. We vary the team's expertise and support levels to measure the impact on the overall project performance. As results, we present our derived productivity index for all scenarios and we state implications found in order to analyze popular preconceptions in GSD area, confirming some, and refusing others. Finally, we foresee ways to extend the models to represent more intricate behaviors and communication patterns that are usually present in globally distributed software projects.

Original languageEnglish
Article number2955093
JournalACM Transactions on Software Engineering and Methodology
Volume25
Issue number3
DOIs
Publication statusPublished - Aug 2016

Keywords

  • Analytical modeling
  • Global software development
  • Performance analysis
  • Stochastic automata networks

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