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A Bayesian Based Method for Agile Software Development Release Planning and Project Health Monitoring

Published: 24 November 2010 Publication History

Abstract

Agile software development (ASD) techniques are iteration based powerful methodologies to deliver high quality software. To ensure on time high quality software, the impact of factors affecting the development cycle should be evaluated constantly. Quick and precise factor evaluation results in better risk assessment, on time delivery and optimal use of resources. Such an assessment is easy to carry out for a small number of factors. However, with the increase of factors, it becomes extremely difficult to assess in short time periods. We have designed and developed a project health measurement model to evaluate the factors affecting software development of the project. We used Bayesian networks (BNs) as an approach that gives such an estimation. We present a quantitative model for project health evaluation that helps decision makers make the right decision early to amend any discrepancy that may hinder on time and high quality software delivery.

Cited By

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  • (2020)Intelligent software engineering in the context of agile software developmentInformation and Software Technology10.1016/j.infsof.2019.106241119:COnline publication date: 1-Mar-2020
  • (2017)Bayesian network model for task effort estimation in agile software developmentJournal of Systems and Software10.1016/j.jss.2017.01.027127:C(109-119)Online publication date: 1-May-2017
  • (2016)Bayesian networks for enhancement of requirements engineeringRequirements Engineering10.1007/s00766-015-0225-321:4(461-480)Online publication date: 1-Nov-2016
  • Show More Cited By

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Published In

cover image Guide Proceedings
INCOS '10: Proceedings of the 2010 International Conference on Intelligent Networking and Collaborative Systems
November 2010
527 pages
ISBN:9780769542782

Publisher

IEEE Computer Society

United States

Publication History

Published: 24 November 2010

Author Tags

  1. Agile Software Development
  2. Bayesian Networks
  3. Decision Support
  4. Intelligent Networks
  5. Software Project Management

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Cited By

View all
  • (2020)Intelligent software engineering in the context of agile software developmentInformation and Software Technology10.1016/j.infsof.2019.106241119:COnline publication date: 1-Mar-2020
  • (2017)Bayesian network model for task effort estimation in agile software developmentJournal of Systems and Software10.1016/j.jss.2017.01.027127:C(109-119)Online publication date: 1-May-2017
  • (2016)Bayesian networks for enhancement of requirements engineeringRequirements Engineering10.1007/s00766-015-0225-321:4(461-480)Online publication date: 1-Nov-2016
  • (2015)A procedure to detect problems of processes in software development projects using Bayesian networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.08.01542:1(437-450)Online publication date: 1-Jan-2015
  • (2013)A model to detect problems on scrum-based software development projectsProceedings of the 28th Annual ACM Symposium on Applied Computing10.1145/2480362.2480560(1037-1042)Online publication date: 18-Mar-2013

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