Software reliability prediction via relevance vector regression

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.