A Comparative Study on Linear Combination Rules for Ensemble Effort Estimation

Abstract

Context: Software effort estimation is a critical factor for project success. A new approach called ensemble effort estimation gets popular because of its performance. While many combination rules have been proposed, they were only compared in a systematic literature review. Objective: To compare linear combination rules proposed in the past studies under the same condition based on empirical approach. Method: We conducted an experiment with 9 linear combination rules, 7 datasets, and 4 effort estimation models. Results: We found 6 out of 9 linear combination rules never underperformed its base learners. No linear combination rule was superior to the others. Conclusion: No definitive rule was found while some linear combination rules can give competitive or better estimates than its base learners.