Analyzing Key Features of Open Source Software Survivability with Random Forest
Open source software (OSS) projects rely on voluntary contributions, but their long-term survivability depends on sustained community engagement and effective problem-solving. Survivability, critical for maintaining project quality and trustworthiness, is closely linked to issue activity, as unresol...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/946 |
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| author | Sohee Park Gihwon Kwon |
| author_facet | Sohee Park Gihwon Kwon |
| author_sort | Sohee Park |
| collection | DOAJ |
| description | Open source software (OSS) projects rely on voluntary contributions, but their long-term survivability depends on sustained community engagement and effective problem-solving. Survivability, critical for maintaining project quality and trustworthiness, is closely linked to issue activity, as unresolved issues reflect a decline in maintenance capacity and problem-solving ability. Thus, analyzing issue retention rates provides valuable insights into a project’s health. This study evaluates OSS survivability by identifying the features that influence issue activity and analyzing their relationships with survivability. Kaplan–Meier survival analysis is employed to quantify issue activity and visualize trends in unresolved issue rates, providing a measure of project maintenance dynamics. A random forest model is used to examine the relationships between project features—such as popularity metrics, community engagement, code complexity, and project age—and issue retention rates. The results show that stars significantly reduce issue retention rates, with rates dropping from 0.62 to 0.52 as stars increase to 4000, while larger codebases, higher cyclomatic complexity, and older project age are associated with unresolved issue rates, rising by up to 15%. Forks also have a nonlinear impact, initially stabilizing retention rates but increasing unresolved issues as contributions became unmanageable. By identifying these critical factors and quantifying their impacts, this research offers actionable insights for OSS project managers to enhance project survivability and address key maintenance challenges, ensuring sustainable long-term success. |
| format | Article |
| id | doaj-art-c724759d628641a99f5981ebc41d222e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c724759d628641a99f5981ebc41d222e2025-01-24T13:21:26ZengMDPI AGApplied Sciences2076-34172025-01-0115294610.3390/app15020946Analyzing Key Features of Open Source Software Survivability with Random ForestSohee Park0Gihwon Kwon1Department of SW Safety and Cyber Security, Kyonggi University, Suwon-si 16227, Gyeonggi-do, Republic of KoreaDepartment of SW Safety and Cyber Security, Kyonggi University, Suwon-si 16227, Gyeonggi-do, Republic of KoreaOpen source software (OSS) projects rely on voluntary contributions, but their long-term survivability depends on sustained community engagement and effective problem-solving. Survivability, critical for maintaining project quality and trustworthiness, is closely linked to issue activity, as unresolved issues reflect a decline in maintenance capacity and problem-solving ability. Thus, analyzing issue retention rates provides valuable insights into a project’s health. This study evaluates OSS survivability by identifying the features that influence issue activity and analyzing their relationships with survivability. Kaplan–Meier survival analysis is employed to quantify issue activity and visualize trends in unresolved issue rates, providing a measure of project maintenance dynamics. A random forest model is used to examine the relationships between project features—such as popularity metrics, community engagement, code complexity, and project age—and issue retention rates. The results show that stars significantly reduce issue retention rates, with rates dropping from 0.62 to 0.52 as stars increase to 4000, while larger codebases, higher cyclomatic complexity, and older project age are associated with unresolved issue rates, rising by up to 15%. Forks also have a nonlinear impact, initially stabilizing retention rates but increasing unresolved issues as contributions became unmanageable. By identifying these critical factors and quantifying their impacts, this research offers actionable insights for OSS project managers to enhance project survivability and address key maintenance challenges, ensuring sustainable long-term success.https://www.mdpi.com/2076-3417/15/2/946Kaplan–Meier survival functionopen source softwarerandom forestsoftware maintenancesurvivability |
| spellingShingle | Sohee Park Gihwon Kwon Analyzing Key Features of Open Source Software Survivability with Random Forest Applied Sciences Kaplan–Meier survival function open source software random forest software maintenance survivability |
| title | Analyzing Key Features of Open Source Software Survivability with Random Forest |
| title_full | Analyzing Key Features of Open Source Software Survivability with Random Forest |
| title_fullStr | Analyzing Key Features of Open Source Software Survivability with Random Forest |
| title_full_unstemmed | Analyzing Key Features of Open Source Software Survivability with Random Forest |
| title_short | Analyzing Key Features of Open Source Software Survivability with Random Forest |
| title_sort | analyzing key features of open source software survivability with random forest |
| topic | Kaplan–Meier survival function open source software random forest software maintenance survivability |
| url | https://www.mdpi.com/2076-3417/15/2/946 |
| work_keys_str_mv | AT soheepark analyzingkeyfeaturesofopensourcesoftwaresurvivabilitywithrandomforest AT gihwonkwon analyzingkeyfeaturesofopensourcesoftwaresurvivabilitywithrandomforest |