# Survivability Prediction of Open Source Software with Polynomial Regression
## 5 Conclusions  
The adoption of OSS in modern software development is increasing, bringing both convenience and challenges. While OSS enhances development efficiency, it can also lead to problems such as project dependency due to maintenance discontinuation resulting from uncertain bug fixes. Consequently, software maintenance costs may rise, prompting users to consider long-term support and maintenance when choosing OSS. Previous studies have focused on OSS development activities or version support [8; 9; 10]. However, unlike these methods, we conducted research focusing on the maintenance status and survivability of OSS based on the activities of contributors and users. In this paper, we proposed predicting OSS survivability using polynomial regression. We conducted research in three stages. In the first stage, data collection and extraction, we collected the characteristics of OSS users, contributors, and code that influence bug-fixing \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Our Study** & **[PERSON] et al. [8]** & **[PERSON] et al. [9]** & **[PERSON] et al. [10]** \\ \hline \multirow{4}{*}{Feature} & Number of Stars and Contributors, LoC, Cognitive complexity, Cyclomatic complexity & Commit, fork, issue, pull request, number of consecutive days without commit, contribution by developer, etc. & 3-tuple (event type, user group, time stamp) event type: any activity that may occur in the repository, such as create, delete, fork, issue, etc. & \\ \hline \multirow{2}{*}{Target} & \(\mathit{Trend}_{\mathit{bug}}\) (Trend of bug-fixing activities) & \multirow{2}{*}{Release presence in the past month} & Analyzes past repository activity data to forecast developmental changes & \multirow{2}{*}{When contributors are next active} \\ \cline{1-1} \cline{3-4}  & Multiple quadratic polynomial regression & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with Other Studies: Machine Learning Techniquesactivities. Additionally, we calculated \(\mathit{Trend}_{bug}\), representing the bug-fixing activity, using the collected data and extracted target data. In the second stage, we performed K-Fold cross-validation for degrees 2 through 5 to identify the degree showing the highest performance and then created and selected the model. In the final stage, we predicted the survivability of OSS using the generated model and validated the results. We applied these three steps to 24 OSS used in KakaoTalk, confirming a predicted decrease in the survival potential of the majority of OSS. Upon examining the repositories of five OSS, it was observed that the maintenance activities such as delayed or discontinued release versions and decreased contributor participation were evident. This suggests that it would be beneficial to use things that have a high probability of survival to prepare for OSS deprecation. We believe that our research can contribute to both OSS users and administrators. Firstly, OSS users can support the selection of OSS for cost savings in projects. Additionally, OSS administrators can receive warnings about the need for solutions to ensure project survival. However, there are still limitations to this method. Issues such as not distinguishing between areas of issues in various OSS and not promptly closing issues were observed. Therefore, careful consideration is needed when applying our method to understand its current state if proper management is not being carried out. To address these limitations, in future research, we aim to differentiate bug issues in different ways for more accurate predictions. Conceptualization, S.P., R.K., and G.K.; Methodology, S.P. and R.K.; Resources, S.P. ; Software, S.P. and R.K.; Validation, R.K. and G.K.; Writing--original draft preparation, S.P. and R.K.; Writing--review and editing, R.K. and G.K.; Visualization, S.P. ; Supervision, G.K. All authors have read and agreed to the published version of the manuscript.