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Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Treeboost (Stochastic Gradient Boosting) model is put forward to predict software effort based on the Use Case Point method. The inputs of the model include software size in use case points, productivity and complexity. A multiple linear regression model was created and the Treeboost model was evaluated against the multiple linear regression model, as well as the use case point model by using four performance criteria: MMRE, PRED, MdMRE and MSE. Experiments show that the Treeboost model can be used with promising results to estimate software effort.

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@inproceedings{DBLP:conf/icmla/NassifCHA12, author = {Ali Bou Nassif and Luiz Fernando Capretz and Danny Ho and Mohammad Azzeh}, title = {A Treeboost Model for Software Effort Estimation Based on Use Case Points}, booktitle = {ICMLA (2)}, year = {2012}, pages = {314-319}, ee = {http://dx.doi.org/10.1109/ICMLA.2012.155}, crossref = {DBLP:conf/icmla/2012-2}, bibsource = {DBLP, http://dblp.uni-trier.de} } @proceedings{DBLP:conf/icmla/2012-2, title = {11th International Conference on Machine Learning and Applications, ICMLA, Boca Raton, FL, USA, December 12-15, 2012. Volume 2}, booktitle = {ICMLA (2)}, publisher = {IEEE}, year = {2012}, isbn = {978-1-4673-4651-1}, ee = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6403616}, bibsource = {DBLP, http://dblp.uni-trier.de} }

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