Metrics-Based Software Reliability Models Using Non-homogeneous Poisson Processes

The traditional software reliability models aim to describe the temporal behavior of software fault-detection processes with only the fault data, but fail to incorporate some significant test-metrics data observed in software testing. In this paper we develop a useful modeling framework to assess the quantitative software reliability with time-dependent covariate as well as software-fault data. The basic ideas employed here are to introduce the discrete proportional hazard model on a cumulative Bernoulli trial process, and to represent a generalized fault-detection processes having time-dependent covariate structure. The resulting stochastic models are regarded as combinations of the proportional hazard models and the familiar non-homogeneous Poisson processes. We compare these metrics-based software reliability models with some typical non-homogeneous Poisson process models, and evaluate quantitatively both goodness-of-fit and predictive performances from the viewpoint of information criteria. As an important result, the accuracy on reliability assessment strongly depends on the kind of software metrics used for analysis and can be improved by incorporating time-dependent metrics data in modeling