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BAHA optimization based feature selection approach for software defect density modeling using S and V transfer function
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AI
DOI：10.1007/s10586-026-06150-5.png)
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Abstract
En 中文
Accurately predicting software defect density (SDD) is crucial during the early stages of software development, as it ensures high-quality software delivery and reduces future maintenance costs. SDD serves as a vital metric for evaluating software quality; however, its prediction is often hindered by the presence of irrelevant and redundant features. Since feature selection is an NP-hard problem, identifying the most relevant attributes significantly enhances predictive performance. To address this, we propose a novel Binary Artificial Hummingbird Algorithm (BAHA) that leverages both S-shaped and V-shaped transfer functions to effectively convert continuous values into binary representations suitable for feature selection tasks. The proposed BAHA is applied to 30 datasets from the PROMISE repository to optimize feature subsets and improve the performance of machine learning regressors. Experimental results demonstrate that BAHA significantly outperforms traditional feature selection methods, achieving substantial improvements in predictive accuracy. The BAHA-XGBoost model achieved the lowest Mean Absolute Error (MAE) of 0.24152, outperforming AHA-XGB by 95.98% and XGB without feature selection by 22.7%. Additional metrics such as MSE (32.13), RMSE (2.10), and R $$^2$$ (0.95) confirm the robustness of the approach. Statistical validation using the Friedman test ranks BAHA as the top-performing optimizer among various metaheuristic techniques, highlighting its superior capability in selecting informative features for SDD prediction.
Keywords:
Software defect density prediction
Feature selection
Binary artificial hummingbird algorithm (BAHA)
Artificial hummingbird algorithm (AHA)
Swarm intelligence
Metaheuristic optimization
Transfer function
Regression
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C
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4.1
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5.0K
Citations:
7.5K