## 5 Empirical Studies
### Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**Results**} \\ \cline{2-3}  & **Most Similar** & **Probability Score** \\  & **Top 10 Elements** & \\ \hline  & DHTUDPPacketRequestFindValue.java & 0.885 \\  & DHTUDPPacketRequestPing.java & 0.870 \\  & DHTUDPPacketRequestFindNode.java & 0.869 \\ Single input query & DHTUDPPacketReplyjava & 0.868 \\ “DHTUDPPacketReply.java & 0.867 \\  & DHTUDPPacketData.java & 0.862 \\  & DHTUDPPacketData.java & 0.862 \\  & DHTUDPPacketRequestToD.java & 0.841 \\  & DHTUDPPacketRequestStore.java & 0.838 \\  & DHTTransportStatsImpl.java & 0.837 \\  & DHTUDPPacketReplyStats.java & 0.809 \\ \hline  & DHTUDPPacketRequestFindValue.java & 0.914 \\ Group input query: & DHTUDPPacketData.java & 0.910 \\
[“DHTTransport”, & DHTUDPPacket.java & 0.909 \\ portUDPjava”, & DHTUDPPacketReplyFindNode.java & 0.906 \\ “DHTUDPPacketReplyPing.java & 0.894 \\  & DHTUDPPacketRequestFinalNode.java & 0.882 \\  & DHTUDPPacketRequestStore.java & 0.881 \\  & DHTUDPPacketRequestOther.java & 0.880 \\  & DHTUDPPUtils.java & 0.880 \\  & DHTUDPPacketRequestPing.java & 0.876 \\  & DHTUDPPacketReplyStats.java & 0.871 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: A sample of random results from the Vuze dataset at the file level, employing single-input queries and group queries with the highest probability score as output. The outcomes are organized in descending order, accompanied by a probability score for each element. Figure 8: A randomly selected data point from the two-dimensional vector representation of Vuze changelogs at file level in the vector space. The denser the dot cluster is, the more files that are close together and the more frequently they were changed together in the past. The results presented in Table 9 demonstrate that elements that have undergone changes together in the past tend to be located close to each other in the vector space, as shown in Figure 8. Additionally, the probability scores for these elements are also higher within the searched range. This means that the higher the score, the more likely it is that changes will propagate to that particular element. This knowledge can assist in managing the complexity and risk associated with making changes to a software system. Comparison between FCP2 Vec and DN  
In this study, a comparative analysis was undertaken to evaluate the performance of FCP2 Vec and the DN with respect to two primary dimensions: change prediction performance and computation efficiency. Change prediction performance: in our study, FCP2 Vec change prediction performance was compared with that of the DN. For consistency, we used the same dataset (Vuze), granularity level, and top \"K\" (where K = 10). Figure 9 illustrates the comparative hit ratios of change prediction for the top 10 for both the DN and FCP2 Vec. The result reveals a superior performance from FCP2 Vec across all DN scenarios [13]. The HR@10 value for FCP2 Vec stands at 0.58, which shows a significant increase compared to the DN scenarios. Specifically, FCP2 Vec's HR@10 value is approximately 92% higher than DN scenario I, about 33% higher than DN scenario II, and approximately 21% higher than DN scenario III. This underscores FCP2 Vec's superior performance in terms of change prediction. The performance outcome was made possible due to the model's capacity to learn from each context, thereby capturing relationships and similarities between elements. In terms of semantic similarity, FCP2 Vec can discern semantic similarity between elements, for instance, elements frequently changed together may have closer vector representations. We leveraged this feature to recommend the next elements based on the developer's historical behavior. Furthermore, we applied the concept of transfer learning. Here, the embedding learned by Word2 vec served as input features for other models. After Word2 vec converted elements into high-dimensional vectors encapsulating the context co-change relationships, these feature vectors were used as input to the UNN algorithm.