Defects prediction on software visualization and hybrid optimization by deep neural network

• Ya Zhang,
• Zhen Li,
• XueGang Yang,
• NingZhong Liu,
• YuXuan Wang,

Vol. 20, No. 2, February 28, 2026
10.3837/tiis.2026.02.001, Download Paper (Free):

• Defect prediction
• RGB image
• software classification
• Deep Learning

Abstract

Newly developed software often encounters challenges in defect prediction using deep learning due to the absence of historical data. This paper introduces a software classification method based on visualization and residual neural networks to select the most suitable data for new software, thereby reducing the adverse effects of inappropriate or mismatched datasets on defect prediction. In the proposed approach, a deep neural network combined with hybrid PSO–SSA hyperparameter optimization is employed to train an optimal dataset and enhance the overall performance of defect prediction.

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