## 1 Introduction
Finally, the authors suggest a future research direction involving the application of unsupervised and semi-supervised learning algorithms, considering that most investigations have focused on supervised learning. [PERSON]. [34] proposes a new method based on Convolutional Neural Networks (CNNs) to identify patterns associated with software defects. Despite the good results presented, it is shown that they are too sensitive to hyperparameters and scope boundaries (focused on an individual version or project) [34]. Therefore, ML techniques  
Figure 1: Typical ML training process to create a model to predict defective software modules. with prediction performances that are less dependent on hyperparameter optimization are valuable in this domain because they empower software testers to become less dependent on ML specialists. Finally, an important aspect related to those studies was pointed out by [29]. According to [29], the perceived efficacy of ML algorithms can vary depending on the performance metrics employed and the specific conditions of the experiment. That issue seems to be related to the use of generic data science metrics for performance evaluation rather than ones more specific to the context of the field domain. Another way to approach the problem is to consider it from the perspective of feature selection (FS). In [33], an FS approach is presented, utilizing the island binary moth flame combined with SVM, NB, and kNN. Ref. [31] proposed a rank aggregation-based Multi-Filter Selection Method, outperforming traditional methods by increasing their prediction accuracies. For example, it increased NBs' accuracy from 76.33% to 81-82% and DTs' from 83.01% to almost 85%. Furthermore, the study suggests that future research should broaden the study's scope to encompass a wider range of prediction models. In [30; 32], both studies demonstrate that the effectiveness of FS methods is influenced by factors such as choice of classifiers, evaluation metrics, and dataset. While FS enhances predictive performance, its efficacy varies across datasets and models, possibly due to class imbalance. While [30] employs only NB and DT, ref. [32] also utilizes kNN and Kernel Logistic Regression (KLR). Attempting to address the issue from another perspective, some studies focus on the quality of the data that the models utilize. [37] proposes a resampling method utilizing NB, but it fails to outperform benchmarks across all datasets, highlighting that there are no universally effective imbalance learning methods; thus, selecting appropriate methods is crucial. Ref. [36] proposes a method and compares it with existing ones, addressing issues with imbalanced learning, such as interference with real data caused by using SMOTE, emphasizing the importance of focusing on the data quality of synthetic data. Lastly, in addressing the imbalance problem, ref. [28] explores the utilization of Generative Adversarial Networks (GANs) for balancing datasets through synthetic sampling of the minority class. Empirical evidence suggests that GANs demonstrate superior performance compared to traditional methods such as SMOTE. However, it is important to note that undersampling techniques with GANs may result in a degradation in prediction performance due to eliminating crucial samples. Moreover, the authors highlight the potential challenges associated with hyperparameter optimization in GAN-based methods and its impact on the final predictive performance of models. Indeed, a critical aspect of those datasets used for training ML model to predict defective modules, including NASA's, is their imbalance. That is because defective modules are expected to be a small ratio of the system. Thus, since the ratio of dataset instances with defective modules is usually much smaller than the non-defective ones, the class imbalance becomes a natural characteristic of those datasets. Proper ML model training compensating for the imbalance with one of the existing techniques must be used.