Software defect prediction with zero-inflated Poisson models
This work addresses software defect prediction for developers, but it is incremental as it applies existing statistical models to a specific dataset.
The paper tackled software defect prediction by applying Poisson and zero-inflated models to the Equinox dataset, finding that zero-inflated models, whether fitted with maximum likelihood or Bayesian methods, performed slightly better based on AIC.
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.