Many statistical algorithms have been proposed for software quality prediction of fault-prone and non fault-prone program modules. The main goal of these algorithms is the improvement of software development processes. In this paper, we introduce a new software prediction algorithm. Our approach is purely Bayesian and is based on finite Dirichlet mixture models. The implementation of the Bayesian approach is done through the use of the Gibbs sampler. Experimental results are presented using simulated data, and a real application for software modules classification is also included.
In this paper, we examine deterministic and Bayesian methods for analyzing finite Dirichlet mixtures. The deterministic method is based on the likelihood approach, and the Bayesian approach is implemented using the Gibbs sampler. The selection of the number of clusters for both approaches is based on the Bayesian information criterion, which is equivalent to the minimum description length. Experimental results are presented using simulated data, and a real application for software modules classification is also included.dirichlet distribution, mixture modeling, maximum likelihood, EM, MDL, BIC, Bayesian analysis, Gibbs sampling, Metropolis-Hastings, software modules,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.