Follow-the-sun methodology in a stochastic modeling perspective

Ricardo M. Czekster*, Paulo Fernandes, Rafael Prikladnicki, Afonso Sales, Alan R. Santos, Thais Webber

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Stochastic modeling of globally distributed projects has become a way to evaluate the performance of teams working in different time zones. The interest in extracting and analyzing quantitative data from geographically dispersed teams has grown throughout the years as major development companies were attracted by potential benefits. We direct our attention to Follow-The-Sun (FTS), a special case of globally distributed projects, where work is handed off every day from one development site to the next on a different time zone. The main benefit of FTS is to reduce project duration providing continuous software development. Our objective in this paper is to discuss a formal mapping of FTS characteristics to a stochastic model in order to predict performance indices of teams such as availability and risk assessment. The modeling effort aims to enhance understanding and feasibility evaluation for FTS projects.

Original languageEnglish
Title of host publicationProceedings - 2011 6th IEEE International Conference on Global Software Engineering Workshops, ICGSE Workshops 2011
Pages54-59
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 6th IEEE International Conference on Global Software Engineering Workshops, ICGSE Workshops 2011 - Helsinki, Finland
Duration: 15 Aug 201118 Aug 2011

Publication series

NameProceedings - 2011 6th IEEE International Conference on Global Software Engineering Workshops, ICGSE Workshops 2011

Conference

Conference2011 6th IEEE International Conference on Global Software Engineering Workshops, ICGSE Workshops 2011
Country/TerritoryFinland
CityHelsinki
Period15/08/1118/08/11

Keywords

  • Follow-the-Sun
  • Global Software Development
  • Performance Evaluation
  • Stochastic Modeling

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