## 2 Literature Review
They also treated the data set into a binary class instead of a multi-class, whereas it is clearly evident from the existing literature [11] that the PROMISE dataset is multi-class. They used all the features to train the classifier instead of selecting the most optimal features set [7] that has more impact on the prediction accuracy. ([PERSON] (Graduate Student Member, IEEE), [PERSON] (Senior Member, IEEE), [PERSON] (Member, IEEE), [PERSON] (Senior Member, IEEE), and [PERSON] (Fellow, IEEE), 2020) [23] proposed a cross version software defect prediction model with data selection. They proposed a novel clustering-based multi-version classifier (CMVC), which can automatically select the most relevant training data by assigning higher weights to those versions which have less noise than others. They classified the output of the classifier into two classes such as clean or buggy. They used the F-Measure as an evaluation metric. However, they did not normalize the dataset in terms of the class imbalance [7; 11; 21], noise [7; 21], and the distribution gap [7; 11; 21]. They also treated the data set into a binary class instead of a multi-class, whereas it is clearly evident from the existing literature [11] that the PROMISE dataset is multi-class. They used all the features to train the classifier instead of selecting the most optimal features set [7] that has more impact on the prediction accuracy. ([PERSON], [PERSON], [PERSON], [PERSON], AND [PERSON], 2020) [1] proposed few-shot learning-based balanced distribution adaptation for heterogeneous defect prediction. They removed the distribution gap between the source and target projects of the NASA repository. They used XGBoost to select the important features before training the classifier. They classified the output of the classifier into binary classes such as clean or buggy. They used AdaBoost as a classifier. They measured their results in terms of the AUC, G, and F measure. However, they did not normalize the dataset in terms of noise [7; 21]. They also treated the data set into a binary class instead of a multi-class, whereas it is clearly evident from the existing literature [11] that the PROMISE dataset is multi-class. Many studies have focused on the issue of attaining a higher accuracy in terms of a cross-project defect prediction. While much research has been conducted on achieving a higher cross-project defect prediction accuracy, only a few researchers have taken the class imbalance, noise, feature selection, and feature extraction together into consideration. Table 1 describes the research summary of our literature review. We built this research paper gap based on our literature review and its summary, which is summarized in Table 1.  
The research gaps of our detailed study are as follows:  
Since it is obvious after the EDA that the PROMISE repository is multiclass [11] and by reducing the distribution gap between the projects [7; 11; 21], removing the noise [7; 21], and resolving the class imbalance [7; 11; 21], a higher cross-project defect prediction accuracy can be achieved. It is also obvious from the existing literature that a higher defect prediction accuracy can be achieved in a cross-project by selecting the optimal features set through XGBoost [7], since XGBoost helps to select the optimal features set. It is obvious from the existing literature [21] that the CNNs property of an auto feature extraction by its pooling layers can help achieve a higher defect prediction accuracy. In addition, it is also obvious from the existing literature that a Softmax layer is better suited