# Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques
## 6 Conclusions and Future Work  
This paper focused on an ideal computing environment to predict refactoring that could promote cost-effective and consistent software design. In the planned model, different software metrics as features, including object-oriented code metrics, were considered to distinguish each refactoring-prone code class and non-refactoring. Considering this aim, \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Author with Reference** & **Classifiers** & **Accuracy** & **F-Measure** & **Recall** & **Precision** \\ \hline [PERSON] et al. [59] & LSTM (Text Based) & 54.3\% & 0.21 & 0.1176 & 1.0 \\  & LSTM (Code metrics) & 40.67\% & 0.67 & 0.0071 & 0.014 \\ \hline  & Random forest & 75\% & 0.81 & 0.75 & 0.75 \\ [PERSON] et al. [59] & Logistic Regression & 47\% & 0.53 & 0.46 & 0.45 \\  & SVM & 44\% & 0.55 & 0.43 & 0.42 \\  & Naive Bayes & 35\% & 0.49 & 0.35 & 0.33 \\ \hline  & Gated Recurrent unit & & & & \\  & Algorithm & 95.91 & 35.23 & 34.26 & 31.84 \\  & with out SMOTE & & & & \\ \hline  & Gated Recurrent unit & & & & \\  & Algorithm & 98.17\% & 100 & 96.44 & 98.11 \\  & with SMOTE & & & & \\ \hline  & LSSVM with out SMOTE & 99.67\% & 0.9958 & — & — \\ \cline{2-6}  & LSSVM with SMOTE & 99.17\% & 0.9958 & — & — \\ \cline{2-6}  & Gaussian & 47.33\% & & & \\  & Bernoulli & 41.33\% & — & — & — \\  & Multinomial & 32\% & & & \\ \hline [PERSON] et. al. [63] & Gaussian & 63.82\% & & & \\  & Bernoulli & 70.66\% & — & — & — \\  & Multinomial & 84.64\% & & & \\ \hline [PERSON] et. al. [64] & SVM with SMOTE & 82\% & — & — & — \\ \cline{2-6}  & Weighted SVM & 88.81\% & & & \\ \hline [PERSON] et. al. [65] & SVM & 88.125 & 0.92 & & \\  & GA+ SVM & 90.25 & 0.948 & & \\  & Whale + SVM & 90.10 & 0.947 & & \\  & GA + Whale + SVM & 90.15 & 0.947 & & \\ \hline [PERSON] [66] & Random Forest & 0.72 & 0.72 & 0.977 & 0.87 \\  & Logistic regression & 0.827 & 0.85 & 0.95 & 0.76 \\ \hline
**Our proposed approach** & & & & & \\ \hline  & Logistic regression & 70.73 & 69.93 & 71.93 & 70.91 \\  & Decision Tree & 69.81 & 71.32 & 73.51 & 72.35 \\  & ANN-GDX & 91.48 & 88.43 & 81.02 & 84.56 \\  & ANN-LM & 91.90 & 83.94 & 84.17 & 84.05 \\  & ANN-GD & 89.38 & 84.61 & 83.91 & 84.25 \\  & SVM-Lin & 69.84 & 70.90 & 70.06 & 70.47 \\  & SVM-Poly & 69.84 & 70.94 & 71.00 & 70.96 \\  & SVM-RBF & 69.84 & 89.91 & 69.99 & 69.94 \\  & LSSVM-Lin & 88.05 & 89.91 & 86.61 & 88.22 \\  & LSSVM-Poly & 89.90 & 88.94 & 89.59 & 89.07 \\  & LSSVM-RBF & 89.73 & 94.31 & 88.92 & 91.53 \\  & ELM-Lin & 90.47 & 90.84 & 90.62 & 90.72 \\  & ELM-Poly & 90.47 & 91.03 & 90.01 & 90.51 \\  & ELM-RBF & 93.50 & 91.07 & 91.10 & 91.08 \\  & MVE & **99.76** & **99.93** & **98.96** & **99.44** \\  & BTE & 99.56 & 99.16 & 98.10 & 98.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: An empirical comparative study of refactoring model at class and method level [State-of-art]. computing a huge number of software metrics that focus on object-oriented programming code characteristics such as coupling, cohesion, complexity, depth, dependency, etc., is considered in this paper. A total of 125 metrics were computed, which need to be processed to select significant features. The proposed model aims to retain only significant features for classification and achieve it, and a multi-phased feature selection method was implemented. The sequential implementation of the Wilcoxon significant test or rank-sum test, Pearson Correlation Test and Principal Component Analysis strengthened the retention of the most important features for further computation and thus achieves higher computational efficiency. In this paper, the data imbalance problem has been resolved through three different sampling methods: random sampling, upsampling, and downsampling. For classification, they provided sufficient training data in conjunction with original samples, performing normalization over final data samples; the proposed model achieved consistent data for subsequent learning and classification.