Self-Adaptive Ensemble -based Approach for Software Effort Estimation

Software Effort Estimation (SEE) is one of the most challenging tasks in software project management. In the literature, the researchers addressed the SEE problem in different ways, including models developed using machine learning (ML). Integrating individual models (Ensemble) is an active research topic in the ML domain, leading to improved performance than the individual models. Ensembling approaches such as bagging, boosting, and stacking have been extensively studied in the literature for improved SEE. The performance of an ensemble model largely depends upon tuning the hyperparameters of each learner and assigning the correct weight to each base learner. To this end, this work proposes a self-adaptive ensemble-based approach that combines hyperparameter tuning and weight assignment problems in a single step and solves them using optimization problems. The proposed model optimizes the hyperparameters of each learner using the particle swarm optimization (PSO) algorithm. Also, the weights of each base learner are optimized using Sequential Least SQuares Programming (SLSQP). An experimental analysis is conducted to evaluate the proposed model over different SEE datasets from PROMISE and ISBSG data repositories. The results show that the proposed ensemble approach performed better than the individual models. Further, we compared our proposed approach with other commonly used ensembling approaches in the literature. We found that the proposed ensemble approach provides better predictive performance than other ensembles.