A Bayesian Model Averaging Method for Software Reliability Assessment

Autor:	Zhaojun Li, Qiumin Yu
Rok vydání:	2020
Předmět:	Computer science Bayesian probability Posterior probability Markov chain Monte Carlo Bayesian inference computer.software_genre Software quality Statistics::Computation symbols.namesake Key (cryptography) symbols Data mining computer Selection (genetic algorithm) Reliability (statistics)
Zdroj:	2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM).
DOI:	10.1109/aparm49247.2020.9209504
Popis:	Due to the complexity and uncertainty of software failure behavior, the selection and application of reliability models can be challenging. To tackle the challenge of software reliability modeling and prediction, researchers have investigated abundant reliability models including combined models consisting of multiple models. This paper proposes to model and assess software reliability using the Bayesian model averaging method. The proposed modeling approach is based on Bayesian theory as well as selecting existing reliability modeling methods as candidate models. The posterior probability of a model being selected is obtained by Bayesian inference. The posterior probabilities are further used as weights to average the candidate base models. The key and difficulty of applying Bayesian model average method lies in the evaluation of weights. MCMC method is used to estimate the weights in BMA for software reliability modeling and assessment.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::67ddf3f243fd22de3db9ba106f8ed16e https://doi.org/10.1109/aparm49247.2020.9209504 Zobrazit plný text záznamu