## 4 Experiments and Results  
### Dataset and Baselines Description  
The goal of EMGSCC is to generate ADLs based on input libraries. Here, to better compare and evaluate the performance of our experiments, we have collected relevant datasets from Libraries.io (Libraries.io: [[https://libraries.io/](https://libraries.io/)]([https://libraries.io/](https://libraries.io/)), accessed on 8 June 2022) and Github (Github: [[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/)), accessed on 8 June 2022), and propose the Dependency Dataset with Domain Information (DDDI). We first explored the famous open-source dataset Libraries.io, and then filtered high-quality Maven projects with their imported dependency libraries from Github. Since EMGSCC integrated the domain tags and functional description corresponding to the dependency, we also crawled that information from the Maven Central Repository (Maven Central Repository: [[https://mvnrepository.com/repos/central](https://mvnrepository.com/repos/central)]([https://mvnrepository.com/repos/central](https://mvnrepository.com/repos/central)), accessed on 8 June 2022). The final DDDI statistics are shown in Table 1. The following describes the preprocessing steps of the dataset:  
1. **Filtering:** Similar to the previous model LibRec [4], we filtered out Maven projects with less than ten total dependency libraries, resulting in 8282 Maven projects that used a total of 4785 different libraries.
2. **Splitting Inputs and Outputs:** For each Maven project, half of the dependency libraries were randomly selected to be the employed items of the current project, and the remaining half were the accompanying items.
3. **Description preprocessing:** In virtue of the Stanford Core NLP tool (Stanford Core NLP: [[https://stanfordnlp.github.io/CoreNLP/](https://stanfordnlp.github.io/CoreNLP/)]([https://stanfordnlp.github.io/CoreNLP/](https://stanfordnlp.github.io/CoreNLP/)), accessed on 8 June 2022), we finally obtained a ready-to-use vocabulary through lowercasing, filtering, lemmatization, word segmentation, etc.
4. **Tokenization:** We tokenized the participants in EMGSCC. As for dependency libraries, each library was treated as a token; as for domain tags and functional descriptions, each unique tag and each description word were treated as a token separately. With incrementally generated dictionaries, we represented the tokens with numerical ids.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Total** & **Number** \\ \hline Total Maven projects & 8282 \\ Total dependency libraries & 4785 \\ Total domain tags & 450 \\ Maven projects in the training set & 6626 \\ Maven projects in the test set & 1656 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset statistics.  
In order to better demonstrate the effectiveness of EMGSCC, we selected LibRec [4] as our comparison object. To the extent of our knowledge, [PERSON] et al. [4] first proposed LibRec, which is widely used to generate accompanying dependency libraries. LibRec uses association rule mining and collaborative filtering and is a typical data-driven model. Comparisons between LibRec and EMGSCC can reflect the differences and superiorities of models at different enhancement levels.