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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 software intensive government acquisition programs includes multiple overlapping software blocks associated with various capabilities. Software reliability researchers have proposed changepoint models to characterize periods of increased defect discovery. However, these models attempt to identify the location of these changepoints after testing has been performed, which is counter-intuitive because conscious decisions such as testing new functionality drive software changepoints. Existing changepoint models are therefore difficult to employ in a predictive manner. To overcome this limitation, this paper proposes a covariate software defect discovery 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 changepoint models are compared with respect to their predictive accuracy and computational efficiency. Our results indicate that the proposed approach is more computationally efficient and enables accurate prediction of the time needed to achieve a desired defect discovery intensity or mean time to failure despite the occurrence of changepoints during software testing. 

Recent research applies soft computing techniques to fit software reliability growth models. However, runtime performance and the distribution of the distance from an optimal solution over multiple runs must be explicitly considered to justify the practical utility of these approaches, promote comparison, and support reproducible research. This paper presents a meta-optimization framework to design stable and efficient multi-phase algorithms for fitting software reliability growth models. The approach combines initial parameter estimation techniques from statistical algorithms, the global search properties of soft computing, and the rapid convergence of numerical methods. Designs that exhibit the best balance between runtime performance and accuracy are identified. The approach is illustrated through nonhomogeneous Poisson process and covariate software reliability growth models, including a cross-validation step on data sets not used to identify designs. The results indicate the nonhomogeneous Poisson process model considered is too simple to benefit from soft computing because it incurs additional runtime with no increase in accuracy attained. However, a multi-phase design for the covariate software reliability growth model consisting of the bat algorithm followed by a numerical method achieves better performance and converges consistently, compared to a numerical method only. The proposed approach supports higher dimensional covariate software reliability growth model fitting suitable for implementation in a tool.