## 2 Literature Review  
Cross-project defect prediction (CPDP) has recently drawn great interest. To predict defects in projects without sufficient training data for CPDP, many researchers tried to build innovative and competitive CPDP models. However, not all studies report good performances of CPDP. Many of the studies reviewed in the literature have established software defect prediction models for estimating the prediction regarding the softwaredevelopment process including effort estimation, resource utilization, and maintainability during the software development lifecycle. ([PERSON], X.-[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]) [11] worked on a cross-project defect prediction using the PROMISE repository. They normalized the data by using the commonly used min-max normalization. They have done feature selection by randomly selecting the features with the same metrics from the datasets. They used a four layered neural network classifier to predict the class of the mapped instances and added Softmax activation on the top of the neural network for a multi-class classification. They constructed the experiment and the result analyses have demonstrated the effectiveness and efficiency of the proposed approach with the G-measure to be 0.66. However, they did not normalize the dataset in terms of the noise [7; 21]. 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. In a series of experiments, ([PERSON], [PERSON] & [PERSON], 2020) [22] proposed the ADCNN model to extract the important features by the CNN. The ADCNN can learn the semantic and structural features from the source code directly and reduce the distribution gap between the source and target projects in order to achieve a higher defect prediction in the cross-project. They solved the issue of noise for a better data normalization. They divide the output into two classes by using the classification method such as logistic regression. They measured the overall experimental results in terms of the F-measure, AUC, and PoffB20. However, they 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 also used all the features to train the classifier instead of selecting the most optimal features set [6] that has more impact on the prediction accuracy. ([PERSON], [PERSON] & [PERSON], 2017) [21] proposed a model and showed the effectiveness of a feature selection by info gain on the defect prediction accuracy in the cross-project. They aimed to converge to an optimal training dataset and at the same time, they considered the effect of a feature selection and the potential noise in the labeling of the datasets. They classify the output into two classes by using the Naive Bayes (NB) classifier. However, they did not normalize the dataset in terms of the class imbalance [7; 11; 21] and distribution gap [7; 11; 21][PERSON] 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[PERSON]  
([PERSON], [PERSON], [PERSON] AND [PERSON], 2020) [7] proposed an improved CNN model which could be able to better learn semantic representations from source-code ASTs for WPDP. They resolved the issue of noise for a data normalization and performed a feature selection. They divided the output into binary classes by using the logistic regression (LR) classifier. The statistical hypothesis test showed that their method was almost significantly better than other machine learning models in terms of the evaluation metrics, such as the F-Measure and G-Measure. 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].