# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
## 1 Introduction
Thus, the efficacy of CPDP models depends on how to minimize these distribution differences between source and target projects [27]. When creating a classifier model, including redundant and unnecessary features can make the final model perform worse [28]. Consequently, a feature selection process must be used to remove these superfluous or unnecessary characteristics [29]. In previous CPDP studies, class imbalance and data distribution difference problems are present. Therefore, a number of factors drive our use of the data balancing method to handle the noisy imbalance nature of the dataset and feature selection technique in order to mitigate distribution differences in software prediction of defects:  
Improve the performance of the model: Unbalanced, noisy datasets and different data distributions can negatively affect the CPDP model's performance by introducing bias,which can cause overfitting and a reduction in generalization, which can un turn lead to incorrect predictions. The synthetic minority oversampling technique with edited nearest neighbors (SMOTE-ENN) can help improve an imbalanced dataset and eliminate noisy and irrelevant instances, and a feature selection approach, such as CFS, can help to minimize data distribution differences and enhance the performance of the model. 1. Better feature representation: Minimizing noise and balancing the dataset to maintain the significant characteristics of the original data and reduce data distribution differences can help to find and choose the most relevant features. This can help the model learn more accurate feature representations and enhance model performance. 2. Reduce overfitting: Imbalanced datasets and different data distribution can lead to overfitting of the model. When data are imbalanced, the model prioritizes the majority class and overlooks the minority class and, when data are distributed differently, the prediction of target data becomes ineffective. Balancing data and reducing noise from the dataset can help overcome the overfitting problem, simplifying the model ij order to learn from the minority class, and feature selection can help in minimizing data distribution differences to prevent model overfitting. This paper presents a unique supervised domain adaptive cross project defect prediction (CPDP) framework termed SCAG-LSTM, which is based on feature selection and class imbalance techniques, to overcome the aforementioned issues. The fundamental goal of SCAG-LSTM is to lessen the problems of distribution differences, class imbalance, and the existence of noisy and irrelevant instances in order to increase the CPDP's predictive performance. To determine the efficacy of the SCAG-LSTM, experiments are carried out on the AEEEM and PROMISE datasets using the widely-used evaluation metrics F1-measure and _AUC_. Based on the experimental data, the SCAG-LSTM performs better than the benchmark techniques. The key contributions of this work are as follows:  
1. In this research, we propose a novel CPDP model, SCAG-LSTM, that integrates SMOTE-ENN, CFS-BFS and Bi-LSTM with Bi-GRU and Attention Mechanism to construct a cross project defect prediction model that enhances software defect prediction performance. 2. We demonstrate that the proposed novel domain adaptive framework reduces the effect of data distribution and class imbalance problems. 3. We optimize the LSTM model with Bi-GRU and Attention Mechanism to efficiently capture semantic and contextual information and dependencies. 4. To verify the efficiency of the proposed approach, we conducted experiments on PROMISE and AEEEM datasets to compare the proposed approach with the existing CPDP methodologies. This paper follows the following structure. Section 2 provides an overview of the relevant CPDP work. The presentation of our research methodology follows in Section 3. The experimental setups are shown in Section 4. The experimental results are presented in Section 5.