# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
## 3 Methodology
### Proposed Features Selection Approach
Figure 9: Bar chart of prediction performance of proposed model and comparing models on AEEEM. ### External Validity

The term \"external validity\" describes a scientific research study's ability to be transferred to other investigations. The primary risk to the external validity of this study is from our attempt to identify and collect diverse datasets from different AEEEM and PROMISE projects. For our evaluation datasets, we chose five open-source Java programs from the PROMISE and five open-source applications from the AEEEM dataset. We will be able to confirm the correctness of our strategy with additional experiments on different datasets. Furthermore, we cannot claim that our findings are broadly applicable. To ensure that the results of this study can be applied to a larger population, future replication is required. ### Construct Validity

Construct validity pertains to the design of the study and its ability to accurately represent the true objective of the investigation. We have used a methodically related work evaluation technique to combat these hazards to our study design. We double-checked and made multiple adjustments to the research questions to make sure the topic was pertinent to the study's objective. The metrics taken into consideration can also endanger our research. We use static code metrics exclusively for fault prediction. As a result, we are unable to say if our findings apply to additional parameters. Nevertheless, much earlier research also commonly used static code metrics. Further research on performance measures is planned. The creation of ML models is another danger. We looked at a number of factors that might have affected the study, such as feature selection, data pre-processing, data balancing techniques, how to train the models, and which features to evaluate. However, the methods used here are accurate enough to guarantee the validity of the study. ### Conclusion Validity

The degree to which the research conclusion is derived in a reasonable manner is referred to as conclusion validity. We conducted numerous experiments on sufficient projects in this study to reduce the risk to the validity of the conclusions. As a result, the outcomes derived from the collected experimental data should be statistically reliable. ## 7 Conclusions and Future Work

Defect prediction and quality assurance are critical in today's quickly changing software development environment to guarantee the stability and dependability of software projects. Predicting errors across many software projects with accuracy and efficiency is a major challenge in this industry. In order to improve the predictive performance of the cross-project defect prediction (CPDP) model, we combined a variety of data preprocessing, feature selection, data sampling, and modeling approaches in our study to propose a comprehensive approach to addressing the difficulties associated with CPDP. To improve the existing state-of-the-art approaches to predicting software defects, we proposed a novel domain adaptive approach, which first integrates CFS-BFS and SMOTE-ENN to overcome the domain adaptive problems related to data distribution differences and imbalance class, enhancing the model's performance and making it more robust and capable of handling real-world software defect prediction scenarios. Furthermore, it optimizes LSTM with Bi-GRU and Attention Mechanism to capture complex patterns and dependencies in the data, while the attention layer provided insights into which features and instances are most influential in making predictions.