Author:
Mohamed Hosni
Affiliation:
MOSI Research Team, ENSAM, University Moulay Ismail of Meknes, Meknes, Morocco
Keyword(s):
Ensemble Effort Estimation, Software Development Effort Estimation, Combiners, Non-Linear Rule.
Abstract:
Effectively managing a software project to deliver a high-quality product primarily depends on accurately estimating the effort required throughout the software development lifecycle. Various effort estimation methods have been proposed in the literature, including machine learning (ML) techniques. Previous attempts have aimed to provide accurate estimates of software development effort estimation (SDEE) using individual estimation techniques. However, the literature on SDEE suggests that there is no commonly superior estimation technique applicable to all software project contexts. Consequently, the idea of using an ensemble approach emerged. An ensemble combines multiple estimators using a specific combination rule. This approach has been investigated extensively in the past decade, with overall results indicating that it can yield better performance compared to other estimation approaches. However, not all aspects of ensemble methods have been thoroughly explored in the literature
, particularly the combination rule used to generate the ensemble’s output. Therefore, this paper aims to shed light on this approach by investigating both types of combiners: three linear and four non-linear. The ensemble learners employed in this study were K-Nearest Neighbors, Decision Trees, Support Vector Regression, and Artificial Neural Networks. The grid search technique was employed to tune the hyperparameters for both the learners and the non-linear combiners. Six datasets were utilized for the empirical analysis. The overall results were satisfactory, as they indicated that the ensemble and single techniques exhibited similar predictive properties, and the ensemble with a non-linear rule demonstrated better performance.
(More)