## 4 Results and Discussion
### RQ4: Effect of the Size of the Description in the Performance of the Translation  
We have included the results of the average value of the metrics comparing the short and long descriptions in Table 16.  
The results presented in Table 16 reveal a consistent performance gap between short and long descriptions across all evaluated translation systems and metrics. In general, short descriptions achieve higher scores in all metrics, including BLEU, COMET, and METEOR, among others. This suggests that machine translation systems are more effective when handling concise inputs, likely due to reduced syntactic complexity and fewer dependencies that need to be preserved across languages. Conversely, long descriptions, which typically involve more technical detail, embedded clauses, and domain-specific terminology, pose \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Description** & **BLEU** & **COMET** & **BLEU** & **RIBES** & **TER** & **YiSi** & **METEOR** & **CHRF** \\ \hline \multirow{2}{*}{ChatGPT} & Short & 0.511 & 0.716 & 0.544 & 0.926 & 0.705 & 0.722 & 0.762 & 0.780 \\  & Long & 0.489 & 0.645 & 0.520 & 0.916 & 0.684 & 0.710 & 0.740 & 0.770 \\ \hline \multirow{2}{*}{Bing} & Short & 0.450 & 0.713 & 0.489 & 0.912 & 0.659 & 0.702 & 0.758 & 0.771 \\  & Long & 0.435 & 0.630 & 0.471 & 0.902 & 0.640 & 0.684 & 0.722 & 0.756 \\ \hline \multirow{2}{*}{DeepL} & Short & 0.527 & 0.733 & 0.554 & 0.924 & 0.705 & 0.735 & 0.770 & 0.793 \\  & Long & 0.495 & 0.649 & 0.523 & 0.915 & 0.681 & 0.714 & 0.743 & 0.778 \\ \hline \multirow{2}{*}{Google} & Short & 0.524 & 0.739 & 0.552 & 0.926 & 0.706 & 0.739 & 0.774 & 0.792 \\  & Long & 0.483 & 0.661 & 0.512 & 0.912 & 0.672 & 0.711 & 0.738 & 0.772 \\ \hline \multirow{2}{*}{Copilot} & Short & 0.470 & 0.644 & 0.501 & 0.898 & 0.647 & 0.676 & 0.707 & 0.742 \\  & Long & 0.448 & 0.573 & 0.478 & 0.889 & 0.629 & 0.663 & 0.686 & 0.735 \\ \hline \multirow{2}{*}{Average} & Short & 0.496 & 0.709 & 0.528 & 0.917 & 0.684 & 0.715 & 0.754 & 0.776 \\  & Long & 0.470 & 0.632 & 0.501 & 0.907 & 0.661 & 0.696 & 0.726 & 0.762 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Means of the results obtained by each translator. Above are those corresponding to short descriptions. Below are those corresponding to long descriptions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Translater** & **BLEU** & **COMET** & **BLEU** & **RIBES** & **TER** & **YiSi** & **METEOR** & **CHRF** \\ \hline \multirow{2}{*}{ChatGPT} & 0.500 & 0.681 & 0.532 & 0.921 & 0.695 & 0.716 & 0.751 & 0.775 & 0.696 \\ Bing & 0.442 & 0.672 & 0.480 & 0.907 & 0.650 & 0.693 & 0.740 & 0.763 & 0.668 \\ DeepL & 0.511 & 0.691 & 0.539 & 0.919 & 0.693 & 0.725 & 0.757 & 0.785 & 0.703 \\ Google Translate & 0.503 & 0.700 & 0.532 & 0.919 & 0.689 & 0.725 & 0.756 & 0.782 & 0.701 \\ Copilot & 0.459 & 0.608 & 0.489 & 0.894 & 0.638 & 0.670 & 0.697 & 0.739 & 0.649 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Count for the consistency test. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **BLEU** & **COMET** & **BLEU** & **RIBES** & **TER** & **YiSi** & **METEOR** & **CHRF** \\ \hline \multirow{2}{*}{ChatGPT} & 3° & 3° & 3° & 1° & 1° & 3° & 3° & 3° \\ Bing & 5° & 4° & 5° & 4° & 4° & 4° & 4° & 4° \\ DeepL & 1° & 2° & 1° & 2° & 2° & 2° & 1° & 1° \\ Google Translate & 2° & 1° & 2° & 3° & 3° & 1° & 2° & 2° \\ Copilot & 4° & 5° & 4° & 5° & 5° & 5° & 5° & 5° \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Similarity ranking of the translations produced by each translator compared to those provided by INCIBE. greater challenges for accurate translation. As a result, the observed drop in performance for longer texts may have implications for the practical application of these tools in cyber-security contexts, where timely and accurate translation of detailed vulnerability reports is crucial.