# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
## 2 Related Work
### Domain Adaptation  
In cross project defect prediction, one typical challenge deals with the problem of imbalanced datasets and the existence of noisy and irrelevant instances among source and target projects. The issue of class imbalance has been noted in a number of fields [35] and significantly impairs prediction model performance in SDP. A number of methods, including undersampling, oversampling, and the synthetic minority oversampling technique (SMOTE), have been developed by researchers to lessen the effects of data imbalance and enhance prediction ability. For two reasons, undersampling strategies are widely utilized to address class imbalance among all approaches: one, they are faster [36], and second, they do not experience overfitting as oversampling techniques do [37]. [PERSON] et al. [38] suggested an undersampling method based on neighborhoods to address the classification dataset's class imbalance. The outcomes of the trial verified that it is quick and effectively addresses problems with class overlaps and imbalance. In order to create growing training datasets with balanced data, [PERSON] et al. [39] presented a class-imbalance learning strategy that makes use of the stratification embedded in the nearest neighbor (STr-NN) concept. They first leverage TCA and then the STr-NN technique is applied on the data to lessen the data distribution difference between the source and target datasets. In this area, managing the different data distributions across projects is another challenge. Using a variety of strategies, including feature selection, feature extraction, ensemble approaches, data preparation, and sophisticated machine learning algorithms, researchers have achieved great progress in this field. These methods seek to maximize computational efficiency and prediction performance while identifying the most relevant features. Results of an appropriate feature selection can shorten learning times, increase learning efficiency, and simplify learning outcomes. To enhance the effectiveness of cross project defect prediction, by employing a technique known as kernel twin support vector machine (DA-KTSVM) to learn the domain adaptation model, [PERSON] et al. [27] attempted to maximize the similarity between the feature distributions of the source and target projects. Assuming that target features were successfully classified by the defect predictor, since they were matched to source instances, he trained the feature generator with the goal of matching distributions between two distinct projects. [PERSON] et al. [40] proposed a novel feature-selection approach that combinesfilter and wrapper techniques to select optimal features using Mutual Information with the Sequential Forward Method and 10-fold cross-validation. They carried out dimensionality reduction using a feature selection technique to enhance accuracy. In the existing CPDP research, although many related issues have been discussed, including class imbalance learning, data distribution difference, features transformation, etc., the issue of noisy and irrelevant occurrences in the source and target domains, which can affect the CPDP performance, has only been briefly examined [26; 27]. Therefore, in order to enhance the effectiveness of cross project defect prediction, we propose a novel supervised domain adaptive framework called SCAG-LSTM. A summary of the related work discussed above is presented in Table 1, listing the datasets, techniques, and evaluation measures used, along with their advantages and limitations.