Orthogonal Defect Classification-A Concept for In-Process Measurements
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Bayesian reliability analysis of complex repairable systems: Research Articles
Applied Stochastic Models in Business and Industry - Innovative Statistical Models in the European Business and Industry
NHPP models for categorized software defects: Research Articles
Applied Stochastic Models in Business and Industry
Bayesian inference for non-homogeneous poisson process models for software reliability
Bayesian inference for non-homogeneous poisson process models for software reliability
IEEE Transactions on Software Engineering
Inference in Hidden Markov Models
Inference in Hidden Markov Models
| Hi-index | 0.03 |
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.