## 5 Empirical Studies
### Results and Discussion
The underlying principle of the UNN was to create \"K\" nearest neighbors that could be utilized for ranking in our evaluation metrics. Consequently, this approach resulted in improved performance compared to the DN. Computation efficiency: we evaluated the computational efficiency of our proposed model by conducting an experiment on the changelogs of Vuze at the file level. At the  
Figure 9: Comparative hit ratios of change prediction for dependency network (DN) and FCP2 Vec in various scenarios at package level. lowest granularity level, they contained more elements than at the package level and a previous study [13] also benchmarked computational efficiency at this level. The result was an escalation in computational complexity for the dependency network model in terms of learning and inference. In our study, we compared the training and inference time of FCP2 Vec with those of the dependency network. We excluded the elapsed optimization time from our comparison, as it was not considered in the previous study. Figure 10a shows the optimization time in relation to different split ratios and the count of transaction files annually. The findings indicate that the elapsed hyperparameter optimization time peaked with a split ratio of 80:10:10 and troughed with a ratio of 85:7.5:7.5, pointing to a rapid convergence. The prompt identification of optimal hyperparameters during optimization confers several benefits, including efficient resource utilization, accelerated experiment and model selection to reduce overfitting risk, and timely prediction. Figure 10b contrasts the elapsed training times using different split ratios and the annual count of transaction files. Our results highlighted that change propagation at the file level trained 30% quicker than the dependency network, a percentage that increased with increasing file counts. Furthermore, our model clocked an inference time of a mere 0.5 s, significantly outpacing the 17 s logged for the dependency network. The result shows that utilizing file level change coupling can uncover concealed correlations that are absent from both the source code and associated documentation. For example, a class A may be modified in conjunction with another class B without having a structural dependence on it. Moreover, it does not require parsing code, making it more lightweight than structural analysis. Its language-independent nature makes it a highly adaptable and optimal choice for research involving a wide range of subject systems developed in different programming languages. The significance of this research lies on its potential to improve the accuracy and scalability of change propagation in large software systems using the proposed approach. By addressing the current limitation of previous methods such as dependency networks, this research can contribute to the development of more reliable and efficient software systems, reducing the costs of maintenance and error correction. This research can also help software developers with a new set of tools and techniques to improve the quality change process and predict the effects of change propagation. Overall, applying change coupling in the software development process can help developers to better understand and manage the complexity and risk associated with making changes to a software system, leading to improved efficiency, reliability, and quality.