Generative Adversarial Symmetry Discovery
Jianke Yang, Robin Walters, et al.
ICML 2023
We describe the use of a latent Markov process governing the parameters of a nonhomogeneous Poisson process (NHPP) model for characterizing the software development defect discovery process. Use of a Markov switching process allows us to characterize non-smooth variations in the rate at which defects are found, better reflecting the industrial software development environment in practice. Additionally, we propose a multivariate model for characterizing changes in the distribution of defect types that are found over time, conditional on the total number of defects. A latent Markov chain governs the evolution of probabilities of the different types. Bayesian methods via Markov chain Monte Carlo facilitate inference. We illustrate the efficacy of the methods using simulated data, then apply them to model reliability growth in a large operating system software component-based on defects discovered during the system testing phase of development. © 2008 Elsevier Ltd. All rights reserved.
Jianke Yang, Robin Walters, et al.
ICML 2023
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
J. LaRue, C. Ting
Proceedings of SPIE 1989
Y.Y. Li, K.S. Leung, et al.
J Combin Optim