  Background: It is widely recognized that software effort estimation is a
regression problem. Model Tree (MT) is one of the Machine Learning based
regression techniques that is useful for software effort estimation, but as
other machine learning algorithms, the MT has a large space of configuration
and requires to carefully setting its parameters. The choice of such parameters
is a dataset dependent so no general guideline can govern this process which
forms the motivation of this work. Aims: This study investigates the effect of
using the most recent optimization algorithm called Bees algorithm to specify
the optimal choice of MT parameters that fit a dataset and therefore improve
prediction accuracy. Method: We used MT with optimal parameters identified by
the Bees algorithm to construct software effort estimation model. The model has
been validated over eight datasets come from two main sources: PROMISE and
ISBSG. Also we used 3-Fold cross validation to empirically assess the
prediction accuracies of different estimation models. As benchmark, results are
also compared to those obtained with Stepwise Regression Case-Based Reasoning
and Multi-Layer Perceptron. Results: The results obtained from combination of
MT and Bees algorithm are encouraging and outperforms other well-known
estimation methods applied on employed datasets. They are also interesting
enough to suggest the effectiveness of MT among the techniques that are
suitable for effort estimation. Conclusions: The use of the Bees algorithm
enabled us to automatically find optimal MT parameters required to construct
effort estimation models that fit each individual dataset. Also it provided a
significant improvement on prediction accuracy.
