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Traditional software reliability growth models only consider defect discovery data, yet the practical concern of software engineers is the removal of these defects. Most attempts to model the relationship between defect discovery and resolution have been restricted to differential equation-based models associated with these two activities. However, defect tracking databases offer a practical source of information on the defect lifecycle suitable for more complete reliability and performance models. This paper explicitly connects software reliability growth models to software defect tracking. Data from a NASA project has been employed to develop differential equation-based models of defect discovery and resolution as well as distributional and Markovian models of defect resolution. The states of the Markov model represent thirteen unique stages of the NASA software defect lifecycle. Both state transition probabilities and transition time distributions are computed from the defect database. Illustrations compare the predictive and computational performance of alternative approaches. The results suggest that the simple distributional approach achieves the best tradeoff between these two performance measures, but that enhanced data collection practices could improve the utility of the more advanced approaches and the inferences they enable.