A probabilistic model for predicting software development effort

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133 Scopus citations

Abstract

Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic models, when compared to traditional point forecast estimation models, is that Bayesian models provide tools for risk estimation and allow decision-makers to combine historical data with subjective expert estimates. In this paper, we use a Bayesian network model and illustrate how a belief updating procedure can be used to incorporate decision-making risks. We develop a causal model from the literature and, using a data set of 33 real-world software projects, we illustrate how decision-making risks can be incorporated in the Bayesian networks. We compare the predictive performance of the Bayesian model with popular nonparametric neural-network and regression tree forecasting models and show that the Bayesian model is a competitive model for forecasting software development effort.

Original languageEnglish (US)
Pages (from-to)615-624
Number of pages10
JournalIEEE Transactions on Software Engineering
Volume31
Issue number7
DOIs
StatePublished - Jul 1 2005

All Science Journal Classification (ASJC) codes

  • Software

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