TY - CHAP

T1 - A probabilistic model for predicting software development effort

AU - Pendharkar, Parag C.

AU - Subramanian, Girish H.

AU - Rodger, James A.

N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2003

Y1 - 2003

N2 - We use the naive 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.

AB - We use the naive 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.

UR - http://www.scopus.com/inward/record.url?scp=33646494541&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33646494541&partnerID=8YFLogxK

U2 - 10.1007/3-540-44843-8_63

DO - 10.1007/3-540-44843-8_63

M3 - Chapter

AN - SCOPUS:33646494541

SN - 354040161X

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 581

EP - 588

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Kumar, Vipin

A2 - Kumar, Vipin

A2 - Gavrilova, Marina L.

A2 - Tan, Chih Jeng Kenneth

A2 - Tan, Chih Jeng Kenneth

A2 - L’Ecuyer, Pierre

PB - Springer Verlag

ER -