# Analyzing Key Features of Open Source Software Survivability with Random Forest
## 5 Conclusions  
This study focused on analyzing issue retention rates to effectively evaluate the survivability of OSS projects. OSS projects thrive on the voluntary participation of contributors and users, but sustaining them in the long term requires active community involvement and problem-solving capabilities. Thus, the duration and retention rate of issues serve as critical indicators for assessing the long-term viability of OSS projects. This research measured issue retention rates based on resolution status and resolution time to analyze the long-term survivability of OSS projects. This research was conducted through the following experimental stages: First, data from 218 OSS projects were collected using the GitHub API. This dataset included various project characteristics, such as the number of stars, forks, contributors, and code complexity. Additionally, issue creation and resolution timestamps were gathered, enabling the measurement of issue retention durations. Second, Kaplan-Meier survival analysis was employed to quantify the distribution of unresolved issue durations, which provided insights into project survivability. Kaplan-Meier survival analysis visualized the proportion of unresolved issues over time, thereby revealing trends in issue resolution rates within OSS projects. The issue retention rates derived from Kaplan-Meier survival functions allowed for a quantitative assessment of each project's maintenance status and community activity. Third, a random forest model was applied to analyze the impact of collected features on issue retention rates. The random forest model effectively captured the nonlinear relationships among features, identifying the key factors influencing issue retention. To optimize the model's performance, hyperparameter tuning was conducted using RandomizedSearchCV. The experimental results identified stars and watchers as the most influential factors of OSS survivability. PDP analysis revealed that as the number of stars increased, community engagement improved, leading to faster issue resolution and lower retention rates. For instance, as the number of stars increased from 1 to 4000, the issue retention rate decreased significantly from approximately 0.62 to 0.52. This finding indicates that strong community interest contributes to enhanced issue resolution and long-term project survivability. Conversely, the number of forks showed a tendency to increase issue retention rates, rising from approximately 0.53 to 0.56 when the number of forks exceeded 500. This suggests that while a high number of contributors can indicate interest, it may also introduce challenges such as coordination difficulties and management complexity. Other variables, such as lines of code (LoC) and the number of contributors, also influenced OSS survivability. LoC showed an upward trend in issue retention rates, increasing from approximately 0.545 to 0.565 as the code size exceeded 20,000 lines. This implies that larger codebases have greater maintenance burdens, slowing issue resolution. The number of contributors initially had a positive impact, reducing issue retention rates, but after reaching approximately 40 contributors, the effect plateaued, suggesting that excessive contributor numbers can lead to management difficulties. Code complexity and project age were also identified as factors negatively impacting survivability. Both metrics showed an increase in issue retention rates as their valuesgrew. For example, a cyclomatic complexity exceeding 2000 led to an increase in retention rates from approximately 0.552 to 0.565. Similarly, the project age showed a sharp rise in retention rates beyond 60 months, highlighting the challenges faced by aging projects and the need for systematic management. This study makes significant contributions by introducing a novel methodology for quantitatively evaluating OSS survivability based on issue retention rates.