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

Abstract

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.