# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
## 3 Methodology
### Proposed Features Selection Approach
We conducted a number of tests on 10 publicly available apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylynn (AEEEM) and predictor models in software engineering (PROMISE) datasets in order to assess the efficacy of the suggested models, i.e., the baseline models, active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), two-phase feature importance amplification method (TFIA), domain adaptive kernel twin support vector machines method (DA-KTSVMO), two-phase transfer learning method (TPTL), and generative adversarial long-short term memory neural networks method (GB-CPDP) and the outcomes were contrasted. According to our findings, the suggested model outperforms the baseline models by 33.03%, 29.15% and 1.48% in terms of F1-measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (_AUC_) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline model F1-measure by 42.60%, 32.00% and 25.10% and _AUC_ by 34.90%, 27.80% and 12.96% on the feature-selected and balanced datasets. Future studies and advancements have multiple directions to pursue. The suggested model can be strengthened and made more robust and generalizable by validating its performance on additional datasets. Secondly, since SCAG-LSTM is limited to one-to-one prediction, we could attempt research on the many-to-one scenarios in the future. Conceptualization, K.J., R.S., M.A. and M.A.W. ; Data curation, M.A. and M.A.W. ; Formal analysis, K.J. ; Funding acquisition, M.A. ; Investigation, R.S. ; Methodology, K.J., R.S. and M.A. ; Project administration, M.A.W. ; Resources, M.A. ; Software, K.J. ; Supervision, R.S. ; Validation, K.J., R.S., M.A. and M.A.W. ; Visualization, M.A.W. ; Writing--original draft, K.J. ; Writing--review and editing, R.S., M.A. and M.A.W. All authors have read and agreed to the published version of the manuscript. This research was supported by EIAS Data Science and Blockchain Lab, Prince Sultan University. Authors would like to thank Prince Sultan University for paying the APC of this article. Data Availability Statement: Data can be shared upon reasonable request from corresponding author. The authors would like to thank Prince Sultan University for its support. The authors declare no conflicts of interest. ## References

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