| Autor: |
Prashanthi, M., Chandra Mohan, M. |
| Předmět: |
|
| Zdroj: |
International Journal of Image & Graphics; Jul2024, Vol. 24 Issue 4, p1-26, 26p |
| Abstrakt: |
The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64%, maximum sensitivity of 95.14%, maximum specificity of 99%, maximum F 1 -score of 93.53%, and maximum precision of 99% by considering the K-fold. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
