## 4 Experiments and Results
### Contribution of Domain Tags and Functional Description  
In order to evaluate the contribution of the domain tags and functional description of the EMGSCC method, this subsection focuses on the following research questions:  
* RQ2: Compared with the baseline model LibRec and the basic EMGSCC, how do domain tags contribute to predicting the accompanying dependency library?
* RQ3: Compared with the EMGSCC with domain tags, how does the functional description contribute to predicting the accompanying dependency library?  
The purpose of RQ2 and RQ3 is to evaluate whether introducing both domain tags and functional descriptions contribute significantly to the overall approach. In order to verify this, we carried out ablation experiments with and without these modules in EMGSCC. We selected LibRec and basic EMGSCC as the baseline model, EMGSCC-Tag as the enhanced model with domain tags, and EMGSCC-Tag-Desc as the enhanced model with domain tags and functional description. We also used the same dataset as RQ1 for our experiments and top-k as our experimental accuracy index.  
In order to explore RQ2, based on the basic EMGSCC model, the experimental results of the EMGSCC-Tag model are shown in Table 4, and the comparison with the basic SS-DLE model and the LibRec model is shown in Figures 5 and 6. Our experimental result shows that EMGSCC-TAG performs better than the basic EMGSCC and LibRec, indicating that the introduction of domain tags enables the generation model to obtain richer representation with domain information and predict higher-quality ADLs.  
Specifically, as shown in Figure 5, on the remaining the metrics except \(Recall@1\), EMGSCC-Tag performs better than EMGSCC, and the improved score is between 0.01 and 0.05. Regarding \(Recall@1\), EMGSCC-Tag and EMGSCC obtain the same score of 0.09. A comparison between EMGSCC-Tag and LibRec is shown in Figure 6. On all metrics, SS-DLE-Tag outperforms LibRec by a score of 0.01 to 0.08. These results demonstrate the importance of introducing domain information to learn potential usage patterns in generating dependency code. Therefore, EMGSCC-Tag can obtain a more accurate vector representation of a dependency library and then recommend more practical ADLs.  
To answer RQ3, we constructed EMGSCC-Tag-Desc, which introduces the functional description of a dependency library on the basis of EMGSCC-Tag. The results are shown in Table 5 and Figure 7. Overall, we find no significant differences between the results of EMGSCC-Tag and EMGSCC-Tag-Desc. Compared with EMGSCC-Tag, EMGSCC-Tag-Desc has a slight improvement on most metrics, yet no obvious improvement can be noticed, indicating that the introduction of functional description does not have a significant impact on the performance of the generation model.  
Specifically, according to Tables 4 and 5 and Figure 7, regarding the metrics \(RecallRate@5\), \(RecallRate@10\), \(Recall@10\), and \(Precision@1\), EMGSCC-Tag-Desc improved by 0.01 compared to EMGSCC-Tag. On the metrics \(Recall@1\), \(Recall@5\), \(Precision@5\), and \(Precision@10\), the EMGSCC-Tag-Desc obtains the same score as the EMGSCC-Tag model. In terms of \(RecallRate@1\), EMGSCC-Tag-Desc is 0.01 less than EMGSCC-Tag.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Top \(k\)** & \(RecallRate@k\) & \(Recall@k\) & \(Precision@k\) \\ \hline top 1 & 0.70 & 0.09 & 0.70 \\ top 5 & 0.92 & 0.34 & 0.55 \\ top 10 & 0.97 & 0.48 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Top \(k\) Accuracy of EMGSCC-Tag.  
Figure 7: The performance comparison between EMGSCC-Tag and EMGSCC-Tag-Desc.