By Topic

Predicting Project Velocity in XP Using a Learning Dynamic Bayesian Network Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Peter Hearty ; Queen Mary University of London-Computer, London ; Norman Fenton ; David Marquez ; Martin Neil

Bayesian networks, which can combine sparse data, prior assumptions and expert judgment into a single causal model, have already been used to build software effort prediction models. We present such a model of an extreme programming environment and show how it can learn from project data in order to make quantitative effort predictions and risk assessments without requiring any additional metrics collection program. The model's predictions are validated against a real world industrial project, with which they are in good agreement.

Published in:

IEEE Transactions on Software Engineering  (Volume:35 ,  Issue: 1 )