Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction.

The increasing importance of deep learning in software development has greatly improved software quality by enabling the efficient identification of defects, a persistent challenge throughout the software development lifecycle. This study seeks to determine the most effective model for detecting defects in software projects. It introduces an intelligent approach that combines Temporal Convolutional Networks (TCN) with Antlion Optimization (ALO). TCN is employed for defect detection, while ALO optimizes the network's weights. Two models are proposed to address the research problem: (a) a basic TCN without parameter optimization and (b) a hybrid model integrating TCN with ALO. The findings demonstrate that the hybrid model significantly outperforms the basic TCN in multiple performance metrics, including area under the curve, sensitivity, specificity, accuracy, and error rate. Moreover, the hybrid model surpasses state-of-the-art methods, such as Convolutional Neural Networks, Gated Recurrent Units, and Bidirectional Long Short-Term Memory, with accuracy improvements of 21.8%, 19.6%, and 31.3%, respectively. Additionally, the proposed model achieves a 13.6% higher area under the curve across all datasets compared to the Deep Forest method. These results confirm the effectiveness of the proposed hybrid model in accurately detecting defects across diverse software projects.