Abstract
Large-scale software exhibits periods of increased defect discovery when blocks of less thoroughly tested code are introduced into an existing codebase. For example, the mission systems schedule of the F-35 Joint Strike Fighter includes multiple overlapping software blocks associated with various capabilities. Software reliability researchers have proposed changepoint models to characterize periods of increased defect discovery, where attempts to identify the location of these changepoints after testing have been performed. This is counterintuitive as conscious decisions drive software changepoints and the models are difficult to employ in a predictive manner. To overcome this limitation, this thesis proposes a covariate software defect detection model capable of explaining changepoints in terms of common software testing activities and metrics such as software size estimation, code coverage, and defect density. The proposed and past models are compared with respect to their predictive accuracy and computational efficiency. Results indicate that the proposed approach enables accurate prediction of the time required to achieve a desired defect discovery intensity or mean time to failure despite the practical challenges posed by changepoints in software.