# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 4 Experiment and Discussion
### Experimental Results and Analysis
#### 4.2.3 Ablation Experiments
Taken together, removing any of the components leads to a decrease in model performance, verifying the key role of tree diagram structural features, CodeBERT semantic features, attention mechanism fusion, and deep clustering on the performance outcomes of the CMVAE model. The same ablation experiments were performed on the unlabeled CodeNetPython800 dataset to obtain the average clustering internal metrics over the dataset with the values shown in Table 5. The experimental results show that the performance of the internal metrics for the clustering results decreases when transitioning from a multi-view to a single-view approach. Among them, the performance is slightly stronger using only the tree diagram structural features, indicating that the tree diagram structural features can provide some support for clustering, while the semantic features have more limited support for clustering in the absence of structural information. The complete CMVAE model combines structural features obtained from code extraction by transforming the tree diagram, CodeBERT semantic features, attention mechanism fusion, and deep clustering module, and performs better on the internal evaluation index of clustering. Although it is slightly inferior to the No-Attention experiment in the DB index, indicating that the attention mechanism may interfere with the inter-cluster separation to some extent, it significantly outperforms the No-Attention experiment in both profile \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ablation Experiment**} & \multicolumn{2}{c}{**PaCon**} & \multicolumn{2}{c}{**Sorting-C\#**} & \multicolumn{2}{c}{**Sorting-Python**} \\ \cline{2-7}  & **Accuracy (\%)** & **FM Index** & **Accuracy (\%)** & **FM Index** & **Accuracy (\%)** & **FM Index** \\ \hline No-CodeBERT & 69.5 & 0.604 & 71.2 & 0.673 & 70.4 & 0.640 \\ No-TreeGraph & 65.2 & 0.647 & 61.5 & 0.592 & 66.4 & 0.577 \\ No-Attention & 75.6 & 0.716 & 75.4 & 0.612 & 75.6 & 0.743 \\ No-DeepClustering & 69.5 & 0.674 & 72.8 & 0.579 & 72.4 & 0.683 \\ CMVAE & 82.4 & 0.793 & 93.9 & 0.876 & 82.9 & 0.687 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment results on labeled datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ablation Experiment** & **Silhouette Coefficient** & **CH Index** & **DB Index** \\ \hline No-CodeBERT & 0.285 & 148.768 & 1.285 \\ No-TreeGraph & 0.228 & 108.155 & 1.359 \\ No-Attention & 0.359 & 457.065 & 1.088 \\ No-DeepClustering & 0.297 & 150.226 & 1.299 \\ CMVAE & 0.390 & 593.795 & 1.118 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment results on unlabeled datasets. coefficient (0.390) and CH index (593.795), indicating that the attention mechanism remains essential in the overall fusion of the representations and the optimization of the clustering performance. It has been demonstrated that the CMVAE model introduced here captures code strategy features more effectively, leading to enhanced clustering performance on unlabeled datasets through the deep fusion of multi-view features and optimization of the clustering process.