## 3 Materials and Methods
### Datasets  
For this study, a database provided by INCIBE (json) has been used, which is available at [8]. The main characteristics of this database are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Metric** & **Type** & **Evaluation** & **Evaluated Aspects** \\ \hline BLEU & Statistical & Lexical & Based on n-gram precision, penalizes short translations. \\ \hline GLEU & Statistical & Lexical & Similar to BLEU, but balances precision and recall. \\ \hline CHRF & Statistical & Lexical & Uses characters instead of words, useful for morphologically rich languages. \\ \hline METEOR & Statistical & Lexical and Semantic & Uses flexible alignment with synonyms and stemming, balancing precision and recall \\ \hline RIBES & Statistical & Word Order & Based on word order, useful for languages with different syntactic structures. \\ \hline TER & Statistical & Edit Distance & Calculates the number of edits required to transform the translation into the reference. \\ \hline YiSi & Quantitative & Semantic & Uses semantic representations to measure similarity between translation and reference. \\ \hline COMET & AI-Based Model & Semantic and Contextual & Neural network-based model that evaluates quality using contextualized embeddings. \\ \hline BERT & AI-Based Model & Semantic & Measures similarity comparing translation and reference embeddings. \\ \hline Word Embeddings & AI-Based Model & Semantic & Measures semantic similarity between sentences using vector representations. \\ \hline \end{tabular}
\end{table}
Table 3: Classification of machine translation evaluation metrics.  
From the above dataset, we have extracted a sample for experimentation. The main characteristics of the sample are included in Table 5. The reason for extracting a sample from the dataset is due to time and cost considerations in the experimentation process.  
Also, we can divide the sample into short and long descriptions of vulnerabilities. Taking the median of 236 characters, there are 3772 vulnerabilities that we call short (number of characters \(\in(0,236]\)), and 3742 descriptions categorized as long (number of characters \(\geq 237\)). We show two examples below.  
The description of the vulnerability identified by the code CVE-2006-0533, containing 160 characters, serves as an example of a short description: _Cross-site scripting (XSS) vulnerability in webmailaging.cgi in cPanel allows remote attackers to inject arbitrary web script or HTML via the numdays parameter_. On the other hand, the description of the vulnerability identified by the code CVE-2006-1056, containing 606 characters, serves as an example of a long description: _The Linux kernel before 2.6.16.9 and the FreeBSD kernel, when running on AMD64 and other 7 th and 8 th generation AuthenticAMD processors, only save/restore the FOP, FIP, and FDP x87 registers in FXSAVE/FXRSTOR when an exception is pending, which allows one process to determine portions of the state of floating point instructions of other processes, which can be leveraged to obtain sensitive information such as cryptographic keys. NOTE: this is the documented behavior of AMD64 processors, but it is inconsistent with Intel processors in a security-relevant fashion that was not addressed by the kernels._