# Analyzing Key Features of Open Source Software Survivability with Random Forest
## 2 Related Works  
Research on OSS survivability has led to the proposal of various methods of assessing and predicting long-term success, focusing on factors such as community engagement, contributor retention, and technical complexity using data from platforms like GitHub. These studies have provided valuable insights into the factors influencing OSS projects' health, but they have often failed to incorporate the temporal aspects of issue resolution and retention, which are critical for a comprehensive evaluation of OSS survivability. This study sought to address these gaps by focusing on issue retention rates and resolution times, offering a more dynamic and time-sensitive approach to evaluating OSS survivability. [PERSON] et al. analyzed the survivability of OSS projects by examining early activity levels, commit records, and organizational involvement [13]. Their findings emphasized that projects with high initial activity and organizational support are more likely to survive in the long term. However, their study focused primarily on early engagement and activity continuity, without exploring how unresolved issues over time affect survivability. By applying Kaplan-Meier analysis to evaluate issue retention rates and resolution times, this study builds on their work by providing a temporal perspective that captures the influence of time-based issue dynamics on project survivability. [PERSON] et al. developed a model to predict OSS project popularity based on features such as forks, issues, and contributors, demonstrating that popularity can drive increased engagement and better maintenance [14]. While their study focused on identifying the predictors of project visibility and engagement, it did not examine how these factors influence long-term survivability. This study extends their findings by integrating issue retention rates and resolution metrics, offering a broader understanding of how community activity and project characteristics impact both short-term engagement and long-term survivability. [PERSON] et al. proposed GitEvolve, a system for predicting user interactions and event patterns in OSS projects using multitask models and graph-based embeddings [15]. Their approach models long-term growth and user contributions by predicting event types and timing, but it does not explore how issue-specific factors influence project survivability. In contrast, this study combined Kaplan-Meier analysis with machine learning to evaluate how temporal metrics like issue resolution times and retention rates affect project health, providing actionable insights for improving OSS maintenance practices. [PERSON] et al. utilized MLP models to predict the survivability of OSS components by identifying software complexity, user base, and developer participation as key factors [16]. Their research highlighted the role of developer engagement in maintaining OSS, but it lacked a detailed examination of how individual factors influence survivability. This study addresses this limitation by employing feature importance analysis and PDP to quantify the relative impact of each feature, offering project managers a clearer understanding of how specific characteristics drive survivability. [PERSON] et al. focused on contributor activity patterns to predict the likelihood of contributor inactivity using survival analysis, developing the GAP tool to monitor and predict contributor engagement [17]. While their research emphasized the importance of retaining contributors for project success, it did not assess broader project features like issue retention or resolution rates. This study distinguishes itself by prioritizing these metrics as critical indicators of OSS survivability, providing a more comprehensive framework for evaluating and improving long-term project health. To address the limitations of prior research, this paper introduces a novel framework that combines Kaplan-Meier survival analysis and machine learning to evaluate issue retention rates, resolution times, and their effects on OSS survivability.