Software Reliability Forecasting: Singular Spectrum Analysis and ARIMA Hybrid Model

In the software reliability growth phase, the nature of the failure data is, in a sense, determined by the software testing process. A hybrid model is proposed for medium and long-term software failure time forecasting in this paper. The hybrid model consists of two methods, Singular Spectrum Analysis (SSA) and Auto Regressive Integrated Moving Average(ARIMA). In this model, the time series of software failure time are firstly decomposed into several sub-series corresponding to some tendentious and oscillation (periodic or quasi-periodic) components and noise by using SSA and then each sub-series is predicted, respectively, through an appropriate ARIMA model, and lastly a correction procedure is conducted for the sum of the prediction results to ensure the superposed residual to be a pure random series. The software failure data of two real projects are analyzed as case studies. The results have been compared with the predictions made by ARIMA and Singular Spectrum Analysis-Linear Recurrent Formulae (SSA-LRF). It shows that the hybrid model has the best performance.