Systematic Mapping Study of Ensemble Effort Estimation

Ensemble methods have been used recently for prediction in data mining area in order to overcome the weaknesses of single estimation techniques. This approach consists on combining more than one single technique to predict a dependent variable and has attracted the attention of the software development effort estimation (SDEE) community. An ensemble effort estimation (EEE) technique combines several existing single/classical models. In this study, a systematic mapping study was carried out to identify the papers based on EEE techniques published in the period 2000-2015 and classified them according to five classification criteria: research type, research approach, EEE type, single models used to construct EEE techniques, and rule used the combine single estimates into an EEE technique. Publication channels and trends were also identified. Within the 16 studies selected, homogeneous EEE techniques were the most investigated. Furthermore, the machine learning single models were the most frequently employed to construct EEE techniques and two types of combiner (linear and non-linear) have been used to get the prediction value of an ensemble.