Outcomes of the TCN-ALO against ANN-PCA.

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.深度学习在软件开发领域的重要性与日俱增，其凭借高效识别缺陷的能力大幅提升了软件质量——而缺陷始终是贯穿软件开发生命周期的一项长期挑战。本研究旨在探寻适用于软件项目缺陷检测的最优模型，提出了一种融合时间卷积网络（Temporal Convolutional Networks，TCN）与蚁狮优化算法（Antlion Optimization，ALO）的智能方法：采用时间卷积网络执行缺陷检测任务，并通过蚁狮优化算法对网络权重进行优化。针对本研究的问题，本文提出了两种模型：(a) 未进行参数优化的基础时间卷积网络模型；(b) 融合时间卷积网络与蚁狮优化算法的混合模型。研究结果表明，在曲线下面积、灵敏度、特异度、准确率以及错误率等多项性能指标上，该混合模型的表现均显著优于基础时间卷积网络模型。此外，该混合模型的表现还优于卷积神经网络（Convolutional Neural Networks）、门控循环单元（Gated Recurrent Units）以及双向长短期记忆网络（Bidirectional Long Short-Term Memory）等前沿基准方法，其准确率分别提升了21.8%、19.6%和31.3%。此外，相较于深度森林（Deep Forest）方法，本文提出的混合模型在所有数据集上的曲线下面积提升了13.6%。上述结果证实了所提混合模型能够在多样化的软件项目中精准检测缺陷，具备良好的应用有效性。