Abstract
We use the naïve Bayes model to forecast software effort. A causal model is developed from the literature, and a procedure to learn Bayesian prior and conditional probabilities is provided. Using a data set of 40 real-life software projects we test our model. Our results indicate that the probabilistic forecasting models allow managers to estimate joint probability distribution over different software effort estimates. A software project manager may use the joint probability distribution to develop a cumulative probability distribution, which in turn may help the manager estimate the uncertainty that the project effort may be greater than the estimated effort.
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© 2003 Springer-Verlag Berlin Heidelberg
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Pendharkar, P.C., Subramanian, G.H., Rodger, J.A. (2003). A Probabilistic Model for Predicting Software Development Effort. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds) Computational Science and Its Applications — ICCSA 2003. ICCSA 2003. Lecture Notes in Computer Science, vol 2668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44843-8_63
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DOI: https://doi.org/10.1007/3-540-44843-8_63
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