<p>Software defect prediction (SDP) models rely on various software metrics and defect data to identify potential defects in new software modules. However, the performance of these predictive models can be negatively impacted by irrelevant, redundant metrics and the imbalanced nature of defect datasets. Additionally, the previous studies mainly use conventional machine learning (ML) techniques, but their predictive performance is not superior enough. Addressing these issues is crucial to improve the accuracy and effectiveness of SDP models. This study presents a novel approach to SDP using a multi-filter wrapper feature selection technique (MFWFS). To identify a subset of relevant and informative features, we leverage the combination of filter techniques—Information gain (IG), Chi-square (CS), and Relief-F (RF) method, and a wrapper technique—Opposition-Based Whale Optimization Algorithm (OBWOA). One-dimensional-Convolutional Neural Network (CNN) with an attention mechanism is employed to enhance the classification performance of the predictive model by efficiently integrating the selected characteristics into abstract deep semantic features. We undertake experiments on seventeen open-source software datasets on four performance measures—AUC, G-mean, F-measure, and MCC and compare the obtained results with existing state-of-the-art ML and hybrid algorithms. The experimental findings demonstrate the greater efficiency of our approach, highlighting the usefulness of the multi-filter wrapper feature selection technique and 1D-CNN with attention to SDP.</p>