## 1 Introduction  
Nowadays, several modern software systems have been developed to meet consumers' increasing demands. Due to the growing number of modern software systems, software reliability has become a critical issue, with modern software being complex and highly defect-prone [1]. Early defect prediction helps in improving the planning and execution of software development projects. Software defects are the main cause of software failures and can cause serious issues (e.g., financial loss) [2]. Therefore, systems developers need to put a considerable amount of effort into testing and debugging to improve overall software reliability. Manual software defect reviews require more time and effort and can detect only 60% of software defects [3].  
Many researchers have successfully employed machine learning models in the software defect prediction process to achieve a better prediction performance and reduce the consumed time and effort [4]. However, these models yield a biased performance on imbalanced software defect datasets towards majority class samples. The number of defective modules (minority class) in imbalanced datasets is much lower than the number of non-defective modules (majority class). In order to handle the class imbalance, several techniques could be employed that can be categorized into different levels, such as data-level, cost-sensitive, ensemble learning, and hybrid-level techniques.  
In this paper, we focus on the data-level and ensemble-learning techniques to handle the software defect imbalance problem. We aim to combine GAN-based methods with boosting ensemble learning to deal with the imbalance issue and enhance the software defect prediction. Our paper's main contributions are: (1) to propose a hybrid approach that can deal with the software defect imbalance problem efficiently by combining GAN-based methods with boosting ensembles and (2) to empirically validate the proposed approach by conducting a comprehensive empirical study to study the effectiveness of our proposed approach against traditional oversampling and undersampling approaches.  
Combining GAN with boosting ensembles has never been investigated in previous software defect prediction studies. Therefore, our main objective in this paper is to develop an approach that combines GAN-based methods with boosting ensembles to address the software defect imbalance problem and enhance overall defect prediction. This approach takes advantage of boosting ensembles and GAN models by employing GAN to generate synthetic samples of the minority class and employs the boosting ensemble to perform the classification task into defective and non-defective modules. To achieve our research objective, we formulated the following research questions:  
* **RQ1**: Will hyperparameter optimization of GAN-based methods yield enhanced defect prediction performance over default hyperparameters?
* **RQ2**: Will utilizing GAN-based methods yield enhanced defect prediction performance over traditional undersampling and oversampling techniques?
* **RQ3**: Will combining GAN-based methods with undersampling yield enhanced defect prediction performance?  
The remaining sections of this paper are organized as follows. Section 2 provides a brief background of the main techniques used in this study. Section 3 discusses the literature review and presents the gap analysis. Section 4 describes the proposed approach for handling imbalance issues in software defect datasets. Section 5 describes the empirical study that we conducted, whereas Section 6 presents and discusses the results of this study. Finally, Section 7 concludes the paper, presents the contributions of this work, and provides direction for future work.