Ensemble Effort Estimation using dynamic selection

The Software Effort Estimation (SEE) process has been approached in different ways in the literature, including models built from Machine Learning (ML). The combination of these models (Ensemble) is an important research topic in ML, and has lead to improvements in accuracy compared to individual models. This paper proposes heterogeneous and dynamic ensemble selection (DES) models, composed by a set of regressors dynamically selected by classifiers to estimate software development effort. In the training phase, a pool of regression algorithms is trained using training data and a validation data set to validate the models. Next, some classifiers are trained to identify the best regression model from the pool for each training instance. In the test phase each trained classifier is used to dynamically select a regressor model from the pool for predicting the effort for each test instance. The final prediction is given by the combination of the predictions of the regressors selected by the classifiers. An experimental analysis considering a relevant set of software effort estimation problems is reported. The experiments demonstrate that the proposed method outperforms individual regressors and some state of the art models of the literature.