Classifier Ensembles for Software Development Team Effort Estimation: A Rapid Systematic Literature Review - Silva, Wilamis Kleiton Nunes da; Duarte, Rafael Batista; Nascimento, Bernan Rodrigues (Anais do Congresso Ibero-Americano em Engenharia de Software (CIbSE), 2026)
This work presents a systematic literature review (SLR) on the use of classifier ensembles for software team effort estimation. The study analyzes the advantages of homogeneous and heterogeneous ensembles, highlighting how the combination of algorithms improves prediction accuracy. A total of 27 relevant studies were examined, using metrics such as MAE, RMSE, and PRED to assess model performance. The results indicate that heterogeneous ensembles are more effective in environments with high variability, while homogeneous ensembles excel in specific domains. The work suggests the exploration of hybrid metrics and dynamic optimization for future research.