Software defect prediction with zero-inflated Poisson models
Software Engineering
2019-10-31 v1 Machine Learning
Abstract
In this work we apply several Poisson and zero-inflated models for software defect prediction. We apply different functions from several R packages such as pscl, MASS, R2Jags and the recent glmmTMB. We test the functions using the Equinox dataset. The results show that Zero-inflated models, fitted with either maximum likelihood estimation or with Bayesian approach, are slightly better than other models, using the AIC as selection criterion.
Cite
@article{arxiv.1910.13717,
title = {Software defect prediction with zero-inflated Poisson models},
author = {Daniel Rodriguez and Javier Dolado and Javier Tuya and Dietmar Pfahl},
journal= {arXiv preprint arXiv:1910.13717},
year = {2019}
}
Comments
4 pages, 2 figures, presented at MadSESE'2019