Systematic literature review of ensemble effort estimation

The need to overcome the weaknesses of single estimation techniques for prediction tasks has given rise to ensemble methods in software development effort estimation (SDEE). An ensemble effort estimation (EEE) technique combines several of the single/classical models found in the SDEE literature. However, to the best of our knowledge, no systematic review has yet been performed with a focus on the use of EEE techniques in SDEE. The purpose of this review is to analyze EEE techniques from six viewpoints: single models used to construct ensembles, ensemble estimation accuracy, rules used to combine single estimates, accuracy comparison of EEE techniques with single models, accuracy comparison between EEE techniques and methodologies used to construct ensemble methods. We performed a systematic review of EEE studies published between 2000 and 2016, and we selected 24 of them to address the questions raised in this review. We found that EEE techniques may be separated into two types: homogeneous and heterogeneous, and that the machine learning single models are the most frequently employed in constructing EEE techniques. We also found that EEE techniques usually yield acceptable estimation accuracy, and in fact are more accurate than single models.