## 5 Evaluation
### Results
#### 5.4.2 Results for RQ2  
To answer RQ2, we evaluate whether the transmission of order-2 feature cross-terms to the DFNN can truly improve the performance prediction accuracy. To verify this, we conduct a comparative analysis between MovePerf that includes order-2 features and a model that does not. In the absence of order-2 features, both the DFNN and FM use the same input data, i.e., the dataset that has undergone standardization processing.  
For both models, we use the same grid search strategy to tune the hyperparameters, ensuring that the process is consistent across both models. This means that the hyperparameter settings are held constant throughout the evaluation so that the only difference between the two models is whether the order-2 feature cross-term is used.  
Table 5 presents the prediction results for MovePerf with order-2 and MovePerf without order-2. On the whole, the model incorporating order-2 features achieves an average error value of 7.69%, representing a 2.79% reduction compared to the model without order-2 features. Furthermore, it demonstrates greater stability with a margin value of 2.15, which is 1.20 lower than that of the baseline model.  
For JUnit, when MovePerf without order-2 is used, there is a significant drop in accuracy. Specifically, the mean increases by 3.67%, and the margin also shows an increase, indicating a decrease in the overall prediction accuracy and stability. This suggests that the order-2 feature interactions play a crucial role in maintaining the model's performance for the JUnit project.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multicolumn{2}{c}{**MovePerf with Order-2**} & \multicolumn{2}{c}{**MovePerf Without Order-2**} \\  & **Mean** & **Margin** & **Mean** & **Margin** \\ \hline JUnit & 5.41 & 0.03 & 9.08 & 2.82 \\ LC-problems & 0.28 & 0.01 & 0.26 & 0.07 \\ Concurrency & 4.92 & 0.02 & 8.99 & 2.07 \\ Kevin & 20.16 & 8.53 & 23.57 & 8.43 \\ \hline Average & 7.69 & 2.15 & 10.48 & 3.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between cases with and without order-2 feature cross-terms.  
For LC-problems, the inclusion of order-2 feature cross-terms leads to a slight increase in the mean by 0.02%, which is a minimal change. However, there is a significant decrease in the margin value, suggesting that the model becomes more stable in its predictions. This indicates that, while the impact on the accuracy is marginal, the order-2 features help to improve the model's consistency and reduce the variance of the predictions.  
For Concurrency, the absence of order-2 feature cross-terms causes a notable drop in accuracy, with the mean increasing by 4.07% and the margin rising by 2.05. This results in a significant decrease in model consistency. In this project, where the complexity of the task is higher, the order-2 feature interactions are particularly important in maintaining stable and accurate predictions.  
For Kevin, the mean of MovePerf without order-2 is 3.41% higher than that of MovePerf, and the margin value is somewhat lower, but this still demonstrates the necessity of order-2 feature interactions.  
Overall, the introduction of order-2 feature cross-terms proves to be beneficial in improving the stability and accuracy of the model, particularly in more complex scenarios like the Concurrency project. The results suggest that order-2 feature interactions are essential in ensuring that the model performs well across a variety of tasks, especially when dealing with higher complexity. Thus, the order-2 feature cross-term in MovePerf is not only necessary overall but also crucial in maintaining the model's predictive power and consistency.