文摘
The aim of software reliability prediction is to estimate future occurrences of software failures to aid in maintenance and replacement. Relevance vector machines (RVMs) are kernel-based learning methods that have been successfully adopted for regression problems. However, they have not been widely explored for use in reliability applications. This study employs a RVM-based model for software reliability prediction so as to capture the inner correlation between software failure time data and the nearest m failure time data. We present a comparative analysis in order to evaluate the RVMs effectiveness in forecasting time-to-failure for software products. In addition, we use the Mann-Kendall test method to explore the trend of predictive accuracy as m varies. The reasonable value range of m is achieved through paired T-tests in 10 frequently used failure datasets from real software projects.