A Probabilistic Model for Predicting Software Development Effort | Zendy

Parag C. Pendharkar | Zendy; Girish H. Subramanian | Zendy; James A. Rodger | Zendy

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A Probabilistic Model for Predicting Software Development Effort

Author(s) -

Parag C. Pendharkar,

Girish H. Subramanian,

James A. Rodger

Publication year - 2003

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

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

Subject(s) - computer science , joint probability distribution , probabilistic logic , software , conditional probability , probability distribution , conditional probability distribution , statistical model , bayesian probability , machine learning , data mining , artificial intelligence , econometrics , statistics , mathematics , programming language

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.

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