## 2 Materials and Methods
### Leveraging Changelog Data in Change Propagation  
Version control is a crucial tool for tracking changes made to a software development project over time. One of the most widely used systems is the concurrent version system (CVS). It creates and maintains a local repository for each project and can push changes to remote or other local repositories as needed. This allows teams to easily undo any changes \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author(s)** & **Year** & **Summary** \\ \hline \multirow{4}{*}{Menghani [20]} & \multirow{4}{*}{2023} & \multirow{4}{*}{
\begin{tabular}{c} Presents a survey of model efficiency in deep learning and the problems therein, moving from modeling methods and infrastructure to hardware, and how hardware directly or indirectly affects the efficiency. \\ \end{tabular} } \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comprehensive overview of machine learning and deep learning in different domains. that may have caused problems. The CVS also allows developers to track changes to files and see who made them. To identify and quantify change coupling, it is necessary to first recover the change history for relevant files. This is often carried out by parsing and analyzing the log of the version control system (VCS). In most cases, it is assumed that if two elements are committed together, they are change coupled. However, older systems such as the CVS that do not support atomic commits may require additional processing to reconstruct change transactions [6]. Git, a renowned distributed version control system, diligently records changes to any assortment of computer files. It is commonly employed to synchronize the endeavors of programmers collectively developing source code for software. Among its objectives are data integrity, speed, and endorsement for distributed, non-linear workflows [26]. Due to the importance of change propagation, research has been carried out to understand, predict, and control how change propagates. Early findings on CP such as in [3] present a model of change propagation during software maintenance and evolution. Impact analysis and change propagation are among the major issues in software change management. [PERSON] [27] introduced an approach to provide impact analysis and change propagation support as an integral part of software engineering environments, so that they can be applied during both software development and maintenance. The prediction of such a change provides a significant challenge in the management of the redesign and customization of complex products, where many change propagation paths may be possible. [PERSON] et al. [28] introduced a method that uses the domain-level behavioral model of a system to analyze change propagation. Researchers have found that change couplings can be identified and evaluated from the log of a VCS using the following three different methods. First, raw counting. Some researchers use a raw counting method to identify change coupling. This method looks at the relationships between changes made to different parts of a system and typically uses a symmetry matrix or co-change matrix data structure. A co-change matrix is a matrix that shows the relationships between changes to different parts of a system, and change couplings are the degree to which changes to one part of a system are connected to or dependent on changes to another part of the system. There are several ways that co-change matrices can be inferred or constructed. One way is to analyze the version history of a system, such as by examining commit logs or other records of changes made to the system over time. By examining which files were modified in each commit, it is possible to construct a co-change matrix that shows which parts of the system were changed together [29; 30; 31]. Second, associated rule. Some researchers, for instance, [10; 32; 33; 34], used association rule mining.