### Abstract

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.

Original language | English (US) |
---|---|

Pages (from-to) | 581-588 |

Number of pages | 8 |

Journal | Lecture Notes in Computer Science |

Volume | 2668 |

State | Published - 2003 |

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### All Science Journal Classification (ASJC) codes

- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science*,

*2668*, 581-588.

}

*Lecture Notes in Computer Science*, vol. 2668, pp. 581-588.

**A probabilistic model for predicting software development effort.** / Pendharkar, Parag C.; Subramanian, Girish; Rodger, James A.

Research output: Contribution to journal › Article

TY - JOUR

T1 - A probabilistic model for predicting software development effort

AU - Pendharkar, Parag C.

AU - Subramanian, Girish

AU - Rodger, James A.

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

M3 - Article

AN - SCOPUS:33646494541

VL - 2668

SP - 581

EP - 588

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -