Applied Sciences (Mar 2024)
Survivability Prediction of Open Source Software with Polynomial Regression
Abstract
In modern software development, OSS (Open Source Software) has become a crucial element. However, if OSS have few contributors and are lacking in maintenance activities, such as bug fixes, are used, it can lead to significant costs and resource allocation due to maintenance discontinuation. Since OSS are developed by a diverse group of contributors, the consistency of their involvement may vary, making continuous support and maintenance unpredictable. Therefore, it is necessary to identify the status of each OSS to avoid increased maintenance costs when selecting OSS for use. To address these issues, we use polynomial regression to predict trends in bug-fixing activities and evaluate the survivability of OSS accordingly. We predict the trend of bug-fixing activities in OSS, using factors such as popularity, number of contributors, and code complexity. A lower trend value indicates more vigorous activity. In this paper, we conduct data collection and extraction, generating model, and model testing and evaluation to predict survivability using these data. After collecting data through various tools, the models of different degrees are generated using K-fold cross-validation. The model with the highest performance is selected based on the RMSE (Root Mean Squared Error) and RSE (Residual Standard Error). Then, the chosen model is employed to predict the survivability of OSS and compare it with actual outcomes. This method is experimented with on OSS used in the KakaoTalk commercial messenger app. As a result, several OSS are predicted to have low survivability, and among them, five are analyzed. It is observed that, in reality, activities such as delayed or discontinued release updates occurred. These findings can support OSS users in selecting OSS for cost-saving purposes and alert OSS administrators to the need for solutions to ensure project survival.
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