The black-box approach based on stochastic software reliability models is a simple methodology with only software fault data in order to describe the temporal behavior of fault-detection processes, but fails to incorporate some significant development metrics data observed in the development process. In this paper we develop proportional intensity-based software reliability models with timedependent metrics, and propose a statistical framework to assess the software reliability with the time-dependent covariate as well as the software fault data. The resulting models are similar to the usual proportional hazard model, but possess somewhat different covariate structure from the existing one. We compare these metrics-based software reliability models with some typical non-homogeneous Poisson process models, which are the special cases of our models, and evaluate quantitatively the goodness-of-fit 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 the time-dependent metrics data in modeling.