# 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  
Ablation experiments were performed on both labeled and unlabeled datasets to assess the effect of various innovative strategies on the CMVAE model. The impact of each component on the final performance was observed and analyzed in detail by removing specific functional parts from the model. Systematically removing key parts of the model helps verify the contribution of each innovation to the model's performance. The specific design of the ablation experiments corresponds to the following four components in the model:  
* Remove CodeBERT Semantic Features (No-CodeBERT): This experiment removed the code semantic features extracted through CodeBERT and only used the tree graph structural features and other functionalities. This experiment evaluates the impact of removing semantic features on model performance. * Remove Tree Graph Structural Features (No-TreeGraph): This experiment removed the code structure features obtained through the centrality measures of the tree graph and only used the semantic features and other functionalities. This design investigates the effect of removing the tree graph structural features on clustering performance. * Remove Attention Mechanism Fusion (No-Attention): This experiment removed the attention mechanism fusion step and relied only on the direct concatenation of structural and semantic features. This design aims to validate the effectiveness of the attention mechanism in fusing multi-view representations. * Remove Deep Clustering Module (No-DeepClustering): This experiment removed the deep clustering module from the model and used only the reconstruction loss from the pre-training phase, applying the k-means algorithm for clustering. This design examines the impact of the deep clustering module on the final clustering results. Table 4 shows the results of the ablation tests performed on the labeled dataset, demonstrating the clustering performance of the model under various ablation experimental configurations using evaluation metrics such as accuracy and FM index. By comparing the results from various experimental settings, the impact of each functional component on the final performance can be observed. After removing any of the code features, the model was converted from a multi-view code representation to a single-view code representation, and there was a reduction in the accuracy and FM index of the clustering results. The removal of the tree graph structural features resulted in a more significant drop in these metrics, indicating that the tree graph has a significant impact in capturing the strategy features of the code. Specifically, when dealing with complex code structures, it provides additional structural information that significantly enhances clustering performance. After removing the fusion component of the attention mechanism, the model's clustering performance deteriorates, resulting in a less effective clustering outcome compared to the complete model. This demonstrates the crucial role of the attention mechanism in integrating multi-view representations and leveraging complementary features. By enhancing the synergistic effect of these features, the attention mechanism ensures that the fused representations are both comprehensive and diverse, thereby improving the model's performance in unsupervised tasks. After removing the deep clustering module and using only the k-means algorithm for direct clustering, the decrease in model performance indicates that the deep clustering module has a significant impact on the final clustering results. By optimizing the cluster center positions and incorporating reconstruction loss during the pre-training stage, deep clustering enhances clustering accuracy, ensuring that the generated viewpoint representations maintain high-quality semantic and structural features.