# Mining on Students' Execution Logs and Repairing Compilation Errors Based on Deep Learning
## 3 Mining on Students' Execution Logs
However, the RS is still capable of generating a small number of code samples for these errors. Error type E5 is more commonly observed in some environments but occurs very rarely in others. The RS is able to accurately capture the error patterns of programmers in different environments, allowing these errors to match their corresponding occurrence frequencies across different environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Error Type**} & \multicolumn{2}{c}{**Raw Dataset**} & \multicolumn{2}{c}{**RS-Generated Dataset**} \\ \cline{2-5}  & **Quantity** & **Proportion** & **Quantity** & **Proportion** \\ \hline E1 & 781 & 17.6\% & 12,515 & 17.5\% \\ E2 & 901 & 20.3\% & 24,679 & 34.6\% \\ E3 & 1039 & 23.4\% & 16,051 & 22.5\% \\ E4 & 451 & 10.1\% & 3008 & 4.2\% \\ E5 & 586 & 13.2\% & 10,218 & 14.3\% \\ E6 & 160 & 3.6\% & 1608 & 2.3\% \\ E7 & 529 & 11.9\% & 3284 & 4.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Chart of error types of SUES-COJ dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **LocalizeAcc** & **SingleAcc** & **RepairAcc** \\ \hline \multirow{3}{*}{TEGCER} & DeepFix & - & - & 27.5\% \\  & DrRepair & 97.7\% & 78.5\% & 70.2\% \\  & ACNPR & **98.9\%** & **83.2\%** & **75.0\%** \\ \hline \multirow{3}{*}{SUES-COJ} & DeepFix & - & - & 29.1\% \\  & DrRepair & 97.9\% & 79.6\% & 72.0\% \\  & ACNPR & **99.5\%** & **84.5\%** & **76.9\%** \\ \hline \multirow{3}{*}{DeepFix} & DeepFix & - & - & 27.0\% \\  & SampleFix & - & - & 45.3\% \\ \cline{1-1}  & DrRepair & 97.9\% & 74.8\% & 66.0\% \\ \cline{1-1}  & ACNPR & **99.2\%** & **84.6\%** & **68.3\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performances of different models. Overall, the RS can effectively simulate various real error types by generating both extremely common errors and rare errors. Unlike perturbation-generated error programs, the programs generated by the RS are not limited by error patterns. It can generate corresponding error types based on different datasets, and the simulated error frequencies for different error types are moderately aligned with the actual frequencies in the given dataset. The quality of the augmented dataset generated using this method is closer to that of the raw dataset. When the raw dataset contains a wider range of error types, the RS-generated error programs also encompass a broader range of information. Consequently, the repair model trained on the augmented dataset will possess more semantic information from error codes and will be capable of fixing a more diverse set of errors. The comparative results of different models trained using perturbation-generated and RS-generated datasets are presented in Table 6. The model trained on training data generated using RS simulation performs slightly worse in terms of LocalizeAcc and SingleAcc compared to the model trained on randomly perturbed data. However, it shows better repair rates on real-world data, with an improvement of 1% to 4% in RepairAcc under the same model architecture. The reason behind this improvement is that RS-generated data captures more complex error patterns and is closer to real-world error patterns compared to randomly perturbed data. By using RS-generated data along with the ACNPR model, the optimal repair rate can be achieved. #### 6.5.3 RQ3: Ablation Experiments

We conducted ablation experiments on three datasets to evaluate the effectiveness of each component. The results of the ablation experiments are shown in Table 7. The base model represents a model that retains only the encoding and decoding layers. The graph attention layer helps the model infer the positions of keywords and variables, thereby improving the model's performance. The capsule network layer consists of two parts: CodeCaps, which extracts deep semantic features from the code, and MsgCaps, which