# Analyzing Key Features of Open Source Software Survivability with Random Forest
## 4 Results and Analysis
### Analysis of Feature Impacts
#### 4.2.2 PDP Analysis  
This section examines the specific effects of each feature on the issue retention rate of OSS projects using PDP analysis. PDP analysis was used to visually demonstrate how the issue retention rate change as the values of individual features increase. A higher y-axis value in the graph indicates an increase in the issue retention rate, suggesting a potential decline in survivability.  
Stars, a key indicator of project popularity, exhibit a significant decrease in the issue retention rate as the x-axis value increases from 0 to 4000,as shown in Figure 7a. Specifically, the y-axis value rapidly drops from approximately 0.62 to 0.52, indicating that less popular projects are more likely to have unresolved issues. After exceeding 4000 stars, the issue retention rate stabilizes, showing minimal changes. This suggests that projects reaching a certain level of popularity tend to achieve stable issue resolution rates.  
For watch, as shown in Figure 7b, the y-axis value increases from approximately 0.54 to 0.58 as the x-axis value grows from 0 to 50. This implies that heightened attention to the project may correlate with an increased likelihood of unresolved issues. Beyond 50 watchers, the issue retention rate tends to stabilize.  
Figure 6: Feature Importance Analysis.  
The LoC, representing the size of the project's codebase, shows an upward trend in the issue retention rate as the x-axis value increases from 0 to 20,000, as shown in Figure 7c. The y-axis value rises from approximately 0.545 to 0.565, indicating that larger codebases may be more prone to unresolved issues. Beyond 20,000 LoC, the retention rate fluctuates slightly but generally remains between 0.550 and 0.565, suggesting increased maintenance challenges with larger codebases.  
The cyclomatic complexity, a measure of code complexity, exhibits a sharp increase in the issue retention rate as the x-axis value grows from 0 to 2000, with the y-axis rising from 0.545 to 0.562 (Figure 7d. However, beyond 6000, the rate stabilizes or slightly decreases, suggesting that projects with high initial complexity may establish better maintenance structures over time.  
Figure 7: Partial dependence plot analysis results for features.  
The project age demonstrates an increasing issue retention rate as the x-axis value grows from 10 to 20, with the y-axis rising from approximately 0.52 to 0.54 (Figure 7e). Around 60 months, the y-axis value increases to approximately 0.55-0.58, indicating that older projects may face challenges in resolving issues promptly, likely due to maintenance difficulties or technical debt.  
Contributors displays a similar pattern. As shown in Figure 7f, the y-axis value decreases slightly when the x-axis value increases from 20 to 40, indicating a temporary improvement in issue resolution. However, beyond 40 contributors, the y-axis begins to rise again, suggesting that larger contributor counts do not necessarily enhance a project's problem-solving capacity. Maintaining an optimal size may be more beneficial for effective management.  
Forks, a measure of community contributions, exhibits a declining issue retention rate as the x-axis value grows from 0 to 500, with the y-axis decreasing from approximately 0.56 to 0.53 (Figure 7g). This indicates that higher contributions generally accelerate issue resolution. However, beyond 500 forks, the retention rate stabilizes, and after 2000 forks, it slightly increases again, suggesting that excessive contributions may introduce additional complexities, potentially hindering issue resolution.