Author:
Mohamed Hosni
Affiliation:
MOSI Research Team, LM2S3 Laboratory, ENSAM, Moulay Ismail Iniversity of Meknes, Meknes, Morocco
Keyword(s):
Support Vector Regression, Kernels, Software Effort Estimation, Ensemble Effort Estimation.
Abstract:
Providing an accurate estimation of the effort required to develop a software project is crucial for its success. These estimates are essential for managers to allocate resources effectively and deliver the software product on time and with the desired quality. Over the past five decades, various effort estimation techniques have been developed, including machine learning (ML) techniques. ML methods have been applied in software development effort estimation (SDEE) for the past three decades and have demonstrated promising levels of accuracy. Numerous ML methods have been explored, including the Support Vector Regression (SVR) technique, which has shown competitive performance compared to other ML techniques. However, despite the plethora of proposed methods, no single technique has consistently outperformed the others in all situations. Prior research suggests that generating estimations by combining multiple techniques in ensembles, rather than relying solely on a single technique,
can be more effective. Consequently, this research paper proposes estimating SDEE using both individual ML techniques and ensemble methods based on SVR. Specifically, four variations of the SVR technique are employed, utilizing four different kernels: polynomial, linear, radial basis function, and sigmoid. Additionally, a homogeneous ensemble is constructed by combining these four variants using two types of combiners. An empirical analysis is conducted on six well-known datasets, evaluating performance using eight unbiased criteria and the Scott-Knott statistical test. The results suggest that both single and ensemble SVR techniques exhibit similar predictive capabilities. Furthermore, the SVR variant with the polynomial kernel is deemed the most suitable for SDEE. Regarding the combiner rule, the non-linear combiner yields superior accuracy for the SVR ensemble.
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