Enhanced Deep Learning Approach for Software Defect Forecasting

In the realm of defect prediction in complex systems, the integration of advanced machine learning techniques holds promise for enhancing accuracy and efficiency. This research introduces a novel approach by combining a Deep Adaptive Neural Network (ARNN) with the optimization capabilities of Cuckoo Search (CSO) for defect and non-defect data classification. Th e proposed model leverages the temporal dependencies within the data using a deep ARNN architecture, allowing for the exploration of intricate patterns and correlations over time. To optimize the model's performance, the Cuckoo Search Optimization algorithm is employed. CSO, inspired by the reproductive behavior of cuckoos, offers an effective mechanism for fine-tuning the parameters of the deep ARNN, optimizing its ability to discern subtle variations between defect and non-defect instances. The efficacy of the proposed approach is evaluated on benchmark datasets, showcasing its capability to achieve superior predictive accuracy compared to conventional methods.