# Mining on Students' Execution Logs and Repairing Compilation Errors Based on Deep Learning
## 3 Mining on Students' Execution Logs
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **LocalizeAcc** & **SingleAcc** & **RepairAcc** \\ \hline \multirow{8}{*}{TEGCER} & DeepFix & - & - & 27.5\% \\  & DeepFix + RS & - & - & 31.4\% \\  & DrRepair & 97.7\% & 78.5\% & 70.2\% \\  & DrRepair + RS & 97.2\% & 76.3\% & 73.3\% \\  & ACNPR & 98.9\% & 83.2\% & 75.0\% \\  & ACNPR + RS & 98.6\% & 80.3\% & 77.9\% \\ \hline \multirow{8}{*}{SUES-COJ} & DeepFix & - & - & 29.1\% \\  & DeepFix + RS & - & - & 33.3\% \\ \cline{1-1}  & DrRepair & 97.9\% & 79.6\% & 72.0\% \\ \cline{1-1}  & DrRepair + RS & 97.6\% & 78.0\% & 75.4\% \\ \cline{1-1}  & ACNPR & 99.5\% & 84.5\% & 76.9\% \\ \cline{1-1}  & ACNPR + RS & 98.1\% & 80.2\% & 77.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of different generated datasets. \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 & 4670 & 16.8\% & 6827 & 11.5\% \\ E2 & 6882 & 24.8\% & 21,395 & 36.0\% \\ E3 & 9720 & 35.0\% & 15,186 & 25.6\% \\ E4 & 519 & 1.9\% & 2915 & 5.0\% \\ E5 & 74 & 0.3\% & 54 & 0.1\% \\ E6 & 591 & 2.1\% & 2490 & 4.2\% \\ E7 & 5346 & 19.2\% & 10,512 & 17.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Chart of error types of TEGCER dataset. further extracts similar error message features. Both parts contribute to improving the repair effectiveness of the model to a certain extent. The experimental results demonstrate that the GA module contributes to an improvement in RepairAcc of 6.7%, 7.6%, and 5.2% for the three datasets, respectively. The capsule network layer contributes to an improvement in RepairAcc of 7.7%, 7.5%, and 4.6% for the same three datasets. In particular, for the TEGCER dataset, the capsule network layer exhibits a larger enhancement in repair accuracy, with the MsgCaps module contributing more than the CodeCaps module. Conversely, for the other datasets, the contributions are reversed. This discrepancy may arise from the higher diversity of errors in the TEGCER dataset, as well as the more complex nature of feedback information provided by the compiler. Hence, the MsgCaps module is better suited to extract deep features from the compiler's descriptive information in this particular case. #### 6.5.4 RQ4: Repair Preferences

To investigate the repair preferences of different modules, we conducted repair validation on the SUES-CO dataset. The experimental results are shown in Table 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Model** & **RepairAcc** \\ \hline \multirow{4}{*}{TEGCER} & Base & 63.5\% \\  & Base + GA & 70.2\% \\  & Base + GA + CodeCaps & 73.3\% \\  & Base + GA + CodeCaps + MsgCaps & 77.9\% \\ \hline \multirow{4}{*}{SUES-COJ} & Base & 62.8\% \\  & Base + GA & 70.4\% \\  & Base + GA + CodeCaps & 75.5\% \\  & Base + GA + CodeCaps + MsgCaps & 77.9\% \\ \hline \multirow{4}{*}{DeepFix} & Base & 58.5\% \\  & Base + GA & 63.7\% \\  & Base + GA + CodeCaps & 67.6\% \\ \cline{1-1}  & Base + GA + CodeCaps + MsgCaps & 68.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of ablation experiments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Error Type** & **Model** & **RepairAcc** \\ \hline \multirow{4}{*}{E1} & Base & 58.5\% \\  & Base + GA & 76.7\% \\  & Base + GA + CodeCaps & 79.5\% \\  & Base + GA + CodeCaps + MsgCaps & 82.2\% \\ \hline \multirow{4}{*}{E2} & Base & 91.8\% \\  & Base + GA & 94.5\% \\  & Base + GA + CodeCaps & 94.9\% \\  & Base + GA + CodeCaps + MsgCaps & 95.3\% \\ \hline \multirow{4}{*}{E3} & Base & 58.5\% \\  & Base + GA & 63.7\% \\  & Base + GA + CodeCaps & 67.6\% \\  & Base + GA + CodeCaps + MsgCaps & 68.3\% \\ \hline \end{tabular}
\end{table} 
Table 8: Results of the repair preferences. The graph attention layer is particularly effective in improving the repair effectiveness for error types that require cross-referencing critical information across code and error description.