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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. [ABSTRACT FROM AUTHOR]

Evaluates the display and rendering factors that contribute to effective communication of three-dimensional shape, especially in dynamic, real-time displays. Operational shape definition; Occluding contours and smooth shading; Specular highlighting; System environment; Paradigm validation; Localized changes in intensity for nearly square objects. INSET: Experimental paradigm..