Improving Software Effort Estimation Through Feature Selection and Optimized SVR

Abstract

Accurate software development effort estimation is essential but often hindered by high-dimensional data and the inefficiencies of handling feature selection and parameter tuning as separate, sequential processes. This study proposes an integrated Whale Optimization Algorithm–Support Vector Regression (WOA-SVR) framework that simultaneously optimizes binary feature selection and continuous SVR hyperparameters (C, γ, ϵ) within a unified search process. Evaluated using the NASA93 dataset under a strict nested 10-fold cross-validation protocol to prevent information leakage, the proposed model's performance was comprehensively assessed using six metrics (MMRE, MdMRE, Pred (25), RMSE, MAE, MAPE), accompanied by mean and standard deviation reporting. A rigorous ablation study empirically proved that simultaneous optimization outperforms sequential approaches. The proposed WOA-SVR successfully eliminated 7 redundant features, reducing the dimensionality from 22 to 15, and achieved an MMRE of 21.32% ± 3.25% and a Pred (25) of 64.52% ± 3.90%. It significantly outperformed Standard SVR, PSO-SVR, GA-SVR, and GWO-SVR. Statistical validation via the Wilcoxon Signed-Rank Test and Cliff’s Delta effect size confirmed large and practically significant improvements. While the results demonstrate that simultaneous optimization provides a robust and simplified alternative for early-stage estimation, the effectiveness is bounded to the NASA93 dataset, necessitating future validation on modern agile repositories.