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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. 

Summary Many non‐homogeneous Poisson process software reliability growth models (SRGM) are characterized by a single continuous curve. However, failures are driven by factors such as the testing strategy and environment, integration testing and resource allocation, which can introduce one or more changepoint into the fault detection process. Some researchers have proposed non‐homogeneous Poisson process SRGM, but only consider a common failure distribution before and after changepoints. This paper proposes a heterogeneous single changepoint framework for SRGM, which can exhibit different failure distributions before and after the changepoint. Combinations of two simple and distinct curves including an exponential and S‐shaped curve are employed to illustrate the concept. Ten data sets are used to compare these heterogeneous models against their homogeneous counterparts. Experimental results indicate that heterogeneous changepoint models achieve better goodness‐of‐fit measures on 60% and 80% of the data sets with respect to the Akaike information criterion and predictive sum of squares measures.